In remote sensing, images have assorted appearances due to fused boundaries, making it difficult ... more In remote sensing, images have assorted appearances due to fused boundaries, making it difficult to generate discriminative feature representation for the purpose of conducting classification tasks. Therefore, an effective feature representation can boost the classification accuracy in the field of satellite imaging. In this paper, we propose a novel hybrid system for satellite image classification that combines the distinct information of different deep features, and generate a discriminative representation by preserving the essential information of original feature space. We use pre-trained convolutional neural networks for extracting our features via transfer learning. For this purpose, we first propose a single strategy where fully connected layers are effectively used to represent different levels of representation of the per image features. Secondly, a robust approach, called entropy controlled neighborhood component analysis, is then proposed to optimize the per fusion of multiple layers of different architectures in a unified hierarchical manner with entropy controlled neighborhood component analysis per. To validate the effectiveness of the proposed approach, we perform experiments on three benchmark satellite datasets; UC MERCED, RS19 and AID. We statistically analyze our results by per with analysis of variance and post-hoc Bonferroni test, and compare our proposed methodology with state-of-the-art methods. Experimental results show that the proposed methodology can accurately classify satellite images with 99.7%, 99.1% and 92.2% accuracy with selected classifier and by utilizing less than 5% features.
Dynamic computer pipelines use feedback loops for iterative computation, due to which, data items... more Dynamic computer pipelines use feedback loops for iterative computation, due to which, data items, while traversing them often collide with one another. These pipelines, therefore, employ specialized mechanisms to detect and avoid such collisions in real-time. Mostly, this is done by deciding when, and when not, to allow newer data items to enter the pipeline. We will show that such decisions may lead to different throughput, and guaranteeing optimal performance is an optimization problem. We begin by building a mathematical model of dynamic pipelines, and make use of genetic algorithm to yield near-optimal throughput. The proposed optimization technique accounts for the hardware overhead, incurred due to the extensions in the pipeline organization, as suggested by itself. Our confidence in the results stems from simulation of 10,0 0 0 dynamic pipeline organizations, and for verification of results, we present hardware implementation of one of those. The proposed framework will specifically be useful in embedded systems for digital signal processing applications.
16. Abstract In this study multidepth deflectometers (MDD) were installed in two in-service aspha... more 16. Abstract In this study multidepth deflectometers (MDD) were installed in two in-service asphaltic concrete highways (one thick, one thin) to measure the pavement response to vehicle loading. A specially configured 3S2 truck was used in the study. It is normally an ...
To accurately extract the dimensions of an object inabsolute dark is of utmost importance f... more To accurately extract the dimensions of an object inabsolute dark is of utmost importance for vision measurementsystem. In this paper, A low cost scanner is been introducedwhich uses Laser Triangulation method to determine the shapeof the object by measuring the reflection from the object surface .A laser beam is passed through glass rod to create a Laser stripefocused on aligned object with in complete dark, video cameracaptured the images in user defined, fixed frames per second. Thedistance to each point can be obtained by means of trigonometriccalculations explicitly with respect to the reference point. Thegenerated 3D cloud will be saved in the form of coordinates. Thegiven points were used to render 3D figure of the object.
The high precedence of epidemiological examination of skin lesions necessitated the well-performi... more The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH2, and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.
ABSTRACT: This paper discusses the analysis of deflection data on thick and thin asphaltic concre... more ABSTRACT: This paper discusses the analysis of deflection data on thick and thin asphaltic concrete pavement sections instrumented with Multidepth Deflectometers (MDD's). Surface and depth deflection data were collected under both Falling Weight Deflectometer (FWD) ...
Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its ... more Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its growth-affecting large number of people worldwide. Recent studies proclaimed that risk factors can be substantially reduced by making it almost treatable, if detected at its early stages. This timely detection and classification demand an automated system, though procedure is quite complex. In this article, a novel strategy is adopted, which not only diagnoses the skin cancer but also assigns a proper class label. The proposed technique is principally built on saliency valuation and the selection of most discriminant deep features selection. The lesion contrast is being enhanced using proposed Gaussian method, followed by color space transformation from RGB to HSV. The new color space facilitates the saliency map construction process, utilizing inner and outer disjoint windows, by making the foreground and background maximally differentiable. From the segmented images, deep features are extracted by utilizing inception CNN model on two basic output layers. These extracted set of features are later fused using proposed decision-controlled parallel fusion method, prior to feature selection using proposed window distance-controlled entropy features selection method. The most discriminant features are later subjected to classification step. To demonstrate the efficiency of the proposed methods, three freely available datasets are utilized such as PH2, ISBI 2016, and ISBI 2017 with achieve accuracy is 97.74%, 96.1%, and 97%, respectively. Simulation results clearly reveal the improved performance of proposed method on all three datasets compared to existing methods.
Wireless capsule endoscopy (WCE) is a new imaging procedure that is used to record internal condi... more Wireless capsule endoscopy (WCE) is a new imaging procedure that is used to record internal conditions of gastrointestinal tract for medical diagnosis. However, due to the presence of bulk of WCE image data, it becomes difficult for the physician to investigate it thoroughly. Therefore, considering aforementioned constraint, lately gastrointestinal diseases are identified by computer-aided methods and with better classification accuracy. In this research, a new computer-based diagnosis method is proposed for the detection and classification of gastrointestinal diseases from WCE images. The proposed approach comprises of four fundamentalsteps:1) HSI color transformation before implementing automatic active contour segmentation; 2) implementation of a novel saliency-based method in YIQ color space; 3) fusion of images using proposed maximizing a posterior probability method; 4) fusion of extracted features, calculated using SVD, LBP, and GLCM, prior to final classification step. We perform our simulations on our own collected datasetcontaining total 9000 samples of ulcer, bleeding and healthy. To prove the authenticity of proposed work, list of statistical measures is considered including classification accuracy, FNR, sensitivity, AUC, and Time. Further, a fair comparison of state-of-the-art classifiers is also provided which will be giving readers a deep inside of classifier's selection for this application. Simulation results clearly reveal that the proposed method shows improved performance in terms of segmentation and classification accuracy.
This paper proposes a multilevel unsupervised image segmentation approach aimed at salient object... more This paper proposes a multilevel unsupervised image segmentation approach aimed at salient object extraction. The fundamental objective is the extrication of pronounced object with confined boundary. Taking full advantage of HSV color space, evident of being nearest colorspace to human perception, proposed methodology segment the image using Expectation Maximization (EM), to estimate the parameters of Gaussian Mixture Model (GMM), using heuristic initialization. The Binary Partitional Tree (BPT) then extract the prime focus from reduced color palette on the basis of largest eigenvector. The final amalgamation with high contrast saliency Map diminishes all inutile fragments and excerpt salient object with ensured boundary. The experimental data indicates that hybrid approach leads to improved color segmentation with the apparent assertion of prime object extraction.
Abstract We propose a framework using artificial neural networks that predicts the IV characteris... more Abstract We propose a framework using artificial neural networks that predicts the IV characteristics of a superconducting thin film with square array of nano-engineered periodic antidots, called holes. We adopt the conventionally used commercial physical properties measurement system to obtain a dataset comprising transport measurements, and use this dataset to train our artificial neural network. Once trained, the model is capable of predicting the curve for varying temperature and magnetic flux values, which are cross validated by the physical properties measurement system. Consistent with the works in literature, our framework suggests Josephson Junctions like behavior near transition temperature and at stronger magnetic fields. Our study is important since repeated measurements using the conventional method are time consuming and costly; we demonstrate that the proposed method may be effectively used to classify the IV characteristics over a wide range of temperature and magnetic field values.
Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from th... more Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK-1, ISIC MSK-2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.
A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to h... more A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to handle substantially high noise with an unknown variance. The impetus of the framework is to preserve the latent detail information of the noisy image while removing the noise with an appropriate smoothing and feasible sharpening. The proposed method is executed in two steps. First, the sub-band replacement and fusion process based on accelerated version of the Bayesian non local means method are implemented to enhance the weak edges that often result in low gradient magnitude and fade out during the de-noising process. Then, a truncated beta-Bernoulli process is employed to infer an appropriate dictionary of the edge enhanced data to obtain de-noising results precisely. Numerical simulations are performed to substantiate the restoration of the weak edges through sub-band replacement and fusion process. The proposed de-noising scheme is validated through visual and quantitative results using well established metrics.
IEEE Journal of Biomedical and Health Informatics, Dec 1, 2021
Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this... more Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas. Furthermore, it can also be useful to limit gratuitous clinical referrals and triage dermatology cases. The objective of this research is to classify the skin lesion image samples, received from different servers. The proposed framework is comprised of two module, which include the skin lesion localization/segmentation and the classification. In the localization module, we propose a hybrid strategy that fuses the binary images generated from the designed 16-layered convolutional neural network model and an improved high dimension contrast transform (HDCT) based saliency segmentation. To utilize maximum information extracted from the binary images, a maximal mutual information method is proposed, which returns the segmented RGB lesion image. In the classification module, a pre-trained DenseNet201 model is re-trained on the segmented lesion images using transfer learning. Afterward, the extracted features from the two fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) method. These resultant features are finally fused using a multi canonical correlation (MCCA) approach and are passed to a multi-class ELM classifier. Four datasets (i.e., ISBI2016, ISIC2017, PH2, and ISBI2018) are employed for the evaluation of the segmentation task, while HAM10000, the most challenging dataset, is used for the classification task. The experimental results in comparison with the state-of-the-art methods affirm the strength of our proposed framework.
Scheduling a sports tournament is a complex optimization problem, which requires a large number o... more Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the new sports events pose their own unique set of requirements, and demand novel constraints. Specifically talking of the strictly time bound events, ensuring fairness between the different teams in terms of their rest days, traveling, and the number of successive games they play, becomes a difficult task to resolve, and demands attention. In this work, we present a similar situation with a recently played sports event, where a suboptimal schedule favored some of the sides more than the others. We introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the others. Furthermore, we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire schedules. The latter is crucial, since successively playing a large number of games may lead to sportsmen burnout, which must be prevented.
International Journal of Imaging Systems and Technology, Feb 17, 2023
The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The... more The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The manual screening of dermoscopic images is not an efficient procedure; therefore, researchers working in the domain of computer vision employed several algorithms to classify the skin lesion. The existing computerized methods have a few drawbacks, such as low accuracy and high computational time. Therefore, in this work, we proposed a novel deep learning and Entropy‐Normal Distribution Optimization Algorithm with extreme learning machine (NDOEM)‐based architecture for multiclass skin lesion classification. The proposed architecture consists of five fundamental steps. In the first step, two contrast enhancement techniques including hybridization of mathematical formulation and convolutional neural network are implemented prior to data augmentation. In the second step, two pre‐trained deep learning models, EfficientNetB0 and DarkNet19, are fine‐tuned and retrained through the transfer learning. In the third step, features are extracted from the fine‐tuned models and later the most discriminant features are selected based on novel Entropy‐NDOELM algorithm. The selected features are finally fused using a parallel correlation technique in the fourth step to generate the result feature vectors. Finally, the resultant features are again down‐sampled using the proposed algorithm and the resultant features are passed to the extreme learning machine (ELM) for the final classification. The simulations are conducted on three publicly available datasets as HAM10000, ISIC2018, and ISIC2019 to achieving an accuracy of 95.7%, 96.3%, and 94.8% respectively.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Human-centric Computing and Information Sciences, Mar 31, 2020
Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has ... more Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. It has been reported [2] that diagnosis of melanoma, in its early stages, significantly increases chances of the patient's survival. Dermatoscopy, also knows as dermoscopy is a non-invasive clinical procedure used for
In remote sensing, images have assorted appearances due to fused boundaries, making it difficult ... more In remote sensing, images have assorted appearances due to fused boundaries, making it difficult to generate discriminative feature representation for the purpose of conducting classification tasks. Therefore, an effective feature representation can boost the classification accuracy in the field of satellite imaging. In this paper, we propose a novel hybrid system for satellite image classification that combines the distinct information of different deep features, and generate a discriminative representation by preserving the essential information of original feature space. We use pre-trained convolutional neural networks for extracting our features via transfer learning. For this purpose, we first propose a single strategy where fully connected layers are effectively used to represent different levels of representation of the per image features. Secondly, a robust approach, called entropy controlled neighborhood component analysis, is then proposed to optimize the per fusion of multiple layers of different architectures in a unified hierarchical manner with entropy controlled neighborhood component analysis per. To validate the effectiveness of the proposed approach, we perform experiments on three benchmark satellite datasets; UC MERCED, RS19 and AID. We statistically analyze our results by per with analysis of variance and post-hoc Bonferroni test, and compare our proposed methodology with state-of-the-art methods. Experimental results show that the proposed methodology can accurately classify satellite images with 99.7%, 99.1% and 92.2% accuracy with selected classifier and by utilizing less than 5% features.
Dynamic computer pipelines use feedback loops for iterative computation, due to which, data items... more Dynamic computer pipelines use feedback loops for iterative computation, due to which, data items, while traversing them often collide with one another. These pipelines, therefore, employ specialized mechanisms to detect and avoid such collisions in real-time. Mostly, this is done by deciding when, and when not, to allow newer data items to enter the pipeline. We will show that such decisions may lead to different throughput, and guaranteeing optimal performance is an optimization problem. We begin by building a mathematical model of dynamic pipelines, and make use of genetic algorithm to yield near-optimal throughput. The proposed optimization technique accounts for the hardware overhead, incurred due to the extensions in the pipeline organization, as suggested by itself. Our confidence in the results stems from simulation of 10,0 0 0 dynamic pipeline organizations, and for verification of results, we present hardware implementation of one of those. The proposed framework will specifically be useful in embedded systems for digital signal processing applications.
16. Abstract In this study multidepth deflectometers (MDD) were installed in two in-service aspha... more 16. Abstract In this study multidepth deflectometers (MDD) were installed in two in-service asphaltic concrete highways (one thick, one thin) to measure the pavement response to vehicle loading. A specially configured 3S2 truck was used in the study. It is normally an ...
To accurately extract the dimensions of an object inabsolute dark is of utmost importance f... more To accurately extract the dimensions of an object inabsolute dark is of utmost importance for vision measurementsystem. In this paper, A low cost scanner is been introducedwhich uses Laser Triangulation method to determine the shapeof the object by measuring the reflection from the object surface .A laser beam is passed through glass rod to create a Laser stripefocused on aligned object with in complete dark, video cameracaptured the images in user defined, fixed frames per second. Thedistance to each point can be obtained by means of trigonometriccalculations explicitly with respect to the reference point. Thegenerated 3D cloud will be saved in the form of coordinates. Thegiven points were used to render 3D figure of the object.
The high precedence of epidemiological examination of skin lesions necessitated the well-performi... more The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH2, and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.
ABSTRACT: This paper discusses the analysis of deflection data on thick and thin asphaltic concre... more ABSTRACT: This paper discusses the analysis of deflection data on thick and thin asphaltic concrete pavement sections instrumented with Multidepth Deflectometers (MDD's). Surface and depth deflection data were collected under both Falling Weight Deflectometer (FWD) ...
Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its ... more Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its growth-affecting large number of people worldwide. Recent studies proclaimed that risk factors can be substantially reduced by making it almost treatable, if detected at its early stages. This timely detection and classification demand an automated system, though procedure is quite complex. In this article, a novel strategy is adopted, which not only diagnoses the skin cancer but also assigns a proper class label. The proposed technique is principally built on saliency valuation and the selection of most discriminant deep features selection. The lesion contrast is being enhanced using proposed Gaussian method, followed by color space transformation from RGB to HSV. The new color space facilitates the saliency map construction process, utilizing inner and outer disjoint windows, by making the foreground and background maximally differentiable. From the segmented images, deep features are extracted by utilizing inception CNN model on two basic output layers. These extracted set of features are later fused using proposed decision-controlled parallel fusion method, prior to feature selection using proposed window distance-controlled entropy features selection method. The most discriminant features are later subjected to classification step. To demonstrate the efficiency of the proposed methods, three freely available datasets are utilized such as PH2, ISBI 2016, and ISBI 2017 with achieve accuracy is 97.74%, 96.1%, and 97%, respectively. Simulation results clearly reveal the improved performance of proposed method on all three datasets compared to existing methods.
Wireless capsule endoscopy (WCE) is a new imaging procedure that is used to record internal condi... more Wireless capsule endoscopy (WCE) is a new imaging procedure that is used to record internal conditions of gastrointestinal tract for medical diagnosis. However, due to the presence of bulk of WCE image data, it becomes difficult for the physician to investigate it thoroughly. Therefore, considering aforementioned constraint, lately gastrointestinal diseases are identified by computer-aided methods and with better classification accuracy. In this research, a new computer-based diagnosis method is proposed for the detection and classification of gastrointestinal diseases from WCE images. The proposed approach comprises of four fundamentalsteps:1) HSI color transformation before implementing automatic active contour segmentation; 2) implementation of a novel saliency-based method in YIQ color space; 3) fusion of images using proposed maximizing a posterior probability method; 4) fusion of extracted features, calculated using SVD, LBP, and GLCM, prior to final classification step. We perform our simulations on our own collected datasetcontaining total 9000 samples of ulcer, bleeding and healthy. To prove the authenticity of proposed work, list of statistical measures is considered including classification accuracy, FNR, sensitivity, AUC, and Time. Further, a fair comparison of state-of-the-art classifiers is also provided which will be giving readers a deep inside of classifier's selection for this application. Simulation results clearly reveal that the proposed method shows improved performance in terms of segmentation and classification accuracy.
This paper proposes a multilevel unsupervised image segmentation approach aimed at salient object... more This paper proposes a multilevel unsupervised image segmentation approach aimed at salient object extraction. The fundamental objective is the extrication of pronounced object with confined boundary. Taking full advantage of HSV color space, evident of being nearest colorspace to human perception, proposed methodology segment the image using Expectation Maximization (EM), to estimate the parameters of Gaussian Mixture Model (GMM), using heuristic initialization. The Binary Partitional Tree (BPT) then extract the prime focus from reduced color palette on the basis of largest eigenvector. The final amalgamation with high contrast saliency Map diminishes all inutile fragments and excerpt salient object with ensured boundary. The experimental data indicates that hybrid approach leads to improved color segmentation with the apparent assertion of prime object extraction.
Abstract We propose a framework using artificial neural networks that predicts the IV characteris... more Abstract We propose a framework using artificial neural networks that predicts the IV characteristics of a superconducting thin film with square array of nano-engineered periodic antidots, called holes. We adopt the conventionally used commercial physical properties measurement system to obtain a dataset comprising transport measurements, and use this dataset to train our artificial neural network. Once trained, the model is capable of predicting the curve for varying temperature and magnetic flux values, which are cross validated by the physical properties measurement system. Consistent with the works in literature, our framework suggests Josephson Junctions like behavior near transition temperature and at stronger magnetic fields. Our study is important since repeated measurements using the conventional method are time consuming and costly; we demonstrate that the proposed method may be effectively used to classify the IV characteristics over a wide range of temperature and magnetic field values.
Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from th... more Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK-1, ISIC MSK-2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.
A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to h... more A hybrid image de-noising framework with an automatic parameter selection scheme is proposed to handle substantially high noise with an unknown variance. The impetus of the framework is to preserve the latent detail information of the noisy image while removing the noise with an appropriate smoothing and feasible sharpening. The proposed method is executed in two steps. First, the sub-band replacement and fusion process based on accelerated version of the Bayesian non local means method are implemented to enhance the weak edges that often result in low gradient magnitude and fade out during the de-noising process. Then, a truncated beta-Bernoulli process is employed to infer an appropriate dictionary of the edge enhanced data to obtain de-noising results precisely. Numerical simulations are performed to substantiate the restoration of the weak edges through sub-band replacement and fusion process. The proposed de-noising scheme is validated through visual and quantitative results using well established metrics.
IEEE Journal of Biomedical and Health Informatics, Dec 1, 2021
Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this... more Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas. Furthermore, it can also be useful to limit gratuitous clinical referrals and triage dermatology cases. The objective of this research is to classify the skin lesion image samples, received from different servers. The proposed framework is comprised of two module, which include the skin lesion localization/segmentation and the classification. In the localization module, we propose a hybrid strategy that fuses the binary images generated from the designed 16-layered convolutional neural network model and an improved high dimension contrast transform (HDCT) based saliency segmentation. To utilize maximum information extracted from the binary images, a maximal mutual information method is proposed, which returns the segmented RGB lesion image. In the classification module, a pre-trained DenseNet201 model is re-trained on the segmented lesion images using transfer learning. Afterward, the extracted features from the two fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) method. These resultant features are finally fused using a multi canonical correlation (MCCA) approach and are passed to a multi-class ELM classifier. Four datasets (i.e., ISBI2016, ISIC2017, PH2, and ISBI2018) are employed for the evaluation of the segmentation task, while HAM10000, the most challenging dataset, is used for the classification task. The experimental results in comparison with the state-of-the-art methods affirm the strength of our proposed framework.
Scheduling a sports tournament is a complex optimization problem, which requires a large number o... more Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the new sports events pose their own unique set of requirements, and demand novel constraints. Specifically talking of the strictly time bound events, ensuring fairness between the different teams in terms of their rest days, traveling, and the number of successive games they play, becomes a difficult task to resolve, and demands attention. In this work, we present a similar situation with a recently played sports event, where a suboptimal schedule favored some of the sides more than the others. We introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the others. Furthermore, we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire schedules. The latter is crucial, since successively playing a large number of games may lead to sportsmen burnout, which must be prevented.
International Journal of Imaging Systems and Technology, Feb 17, 2023
The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The... more The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The manual screening of dermoscopic images is not an efficient procedure; therefore, researchers working in the domain of computer vision employed several algorithms to classify the skin lesion. The existing computerized methods have a few drawbacks, such as low accuracy and high computational time. Therefore, in this work, we proposed a novel deep learning and Entropy‐Normal Distribution Optimization Algorithm with extreme learning machine (NDOEM)‐based architecture for multiclass skin lesion classification. The proposed architecture consists of five fundamental steps. In the first step, two contrast enhancement techniques including hybridization of mathematical formulation and convolutional neural network are implemented prior to data augmentation. In the second step, two pre‐trained deep learning models, EfficientNetB0 and DarkNet19, are fine‐tuned and retrained through the transfer learning. In the third step, features are extracted from the fine‐tuned models and later the most discriminant features are selected based on novel Entropy‐NDOELM algorithm. The selected features are finally fused using a parallel correlation technique in the fourth step to generate the result feature vectors. Finally, the resultant features are again down‐sampled using the proposed algorithm and the resultant features are passed to the extreme learning machine (ELM) for the final classification. The simulations are conducted on three publicly available datasets as HAM10000, ISIC2018, and ISIC2019 to achieving an accuracy of 95.7%, 96.3%, and 94.8% respectively.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Human-centric Computing and Information Sciences, Mar 31, 2020
Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has ... more Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. It has been reported [2] that diagnosis of melanoma, in its early stages, significantly increases chances of the patient's survival. Dermatoscopy, also knows as dermoscopy is a non-invasive clinical procedure used for
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