Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2016
The paper presents a Feed-forward back-propagation Artificial Neural Network (ANN) model for detection of breast cancer using Image Registration Techniques. Gray Level Co-occurrence Matrix (GLCM) features extracted from the known mammogram images are used to train the ANN based detection system. The ANN based detection system will be investigated for different number of neurons and layers on the basis of Mean Square Error (MSE) and optimum number of neurons and layers will be chosen.
2019
Breast cancer is one of the leading cancers among women in developed countries including India. Early diagnosis of the cancer allows treatment whi ch could lead to high survival rate or avoids further clinical evaluation or breast biopsy reducing the unnecessary expenditure. This paper aims to build Artificial Neural Network (ANN) model for detection of breast cancer based on Image Registration techniques. Gray Level Co-occurrence Matrix (GLCM) features are extracted and are used to train the ANN. The performance is analysed on the basis of Mean S quare Error (MS E) for different number of neurons of ANN.
Breast cancer is the second most common reason of women’s deaths happening due to cancer. Early diagnosis of this disease permits treatment which enhances survival rate or avoids further clinical evaluation or breast biopsy diminishing the unnecessary expenditure. Mammography is right now the best technique for identification of breast cancer. Survival rate can be enhanced by detecting the breast cancer in early stages by using Mammogram Images. This paper presents the use of Image Registration Techniques for the enhancement of the effectiveness of interpreting digital mammograms by Artificial Neural Networks. Different Network architectures are employed for breast cancer diagnosis and their accuracy is investigated and compared.
Breast cancer is very common and is considered as the second dangerous disease all over the world due to its death rate. Affected can survive if the disease diagnoses before the appearance of major physical changes in the body. Now a day, mammographic (X-ray of breast region) images are widely used for premature revealing of breast cancer. Aim of the proposed system is to design a Computer Aided Diagnosis system (CAD) used to distinguish between benign (non-cancerous) and malignant (cancerous) mammogram. CAD system are used to help radiologist to increase his diagnosis accuracy. In the proposed system, texture features from mammogram were calculated using Gray Level Co-occurrence Matrix (GLCM) along 0°, from the calculate features most effective features having large contribution to achieve the desired output were chosen and applied to Artificial Neural Network (ANN) for training and classification, as ANN is widely use in various field such as, pattern recognition, medical diagnosis, machine learning and so on. For this research work mini-MIAS database is used and the overall sensitivity, specificity and accuracy achieved by using the proposed system is 99.3%, 100% and 99.4% respectively.
Indian Journal of Science and Technology, 2012
In this paper, we propose a complementary technique of breast c a nc er diagnosis that covers five stages of breast cancer detection based on mammography, which solves many of the problems found otherwise. We also show a very large area w h e r e many methods and techniques can be successfully merged in order to obtain a useful result for human use. These include scaling of the image, removing small objects, smoothing, extracting features, ROI extraction and many image processing techniques. Besides, neural networks are used here to train the system to detect cancer according to the dataset. This combination of multiple techniques can solve problems of the breast cancer detection with a high degree of accuracy. Examples and comparisons are given to illustrate and prove this method.
2020
Breast tumor is the main cause for death amongst women. The aim of this project is plan and contrivance a MATLAB created image processing structure to extract features of breast cancer images in order to classify breast cancer through neural network from mammogram x-rays image (MXI). Breast Cancer (BC) happens several of the most common reasons of mortality including women international. Therefore, this development arranges the organization in preparing the finding of the disease computerized so that further and more citizens may obtain it diagnosed in the early hours so as become treated. Therefore, the experimental finding supports to protect the natural life of the ladies. Brest imaging is the elementary diagnosis for chest disease. It contain several articles that unhelpfully effects in finding of the breast tumour. The indications of recognition exist areas and small scale organization bands that are essential in quick exposure of breast tumour. This system has also the feature...
2010
In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Three layers artificial neural network (ANN) with seven features was proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist's sensitivity 75%.
WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, 2021
Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vect...
Breast cancer is the most common malignancy of women and is the second most common and leading cause of cancer deaths among them. At present, there are no effective ways to prevent breast cancer, because its cause is not yet fully known. Early detection is an effective way to diagnose and manage breast cancer can give a better chance of full recovery. This paper gives a clear idea of classification from the mammogram image to find cancer affected area which is a crucial step in breast cancer detection. The output of the classifier differentiates the normal, benign and malignant cases from applied digital mammographic images.
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
The goal of this paper is to detect the breast cancer using neural networks. Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWT). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient. Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification.
This paper presents a computer aided diagnosis (CAD) system for automatic classification of breast masses in digital mammograms. Initially, Digital mammogram is pre-processed by 2D-median filter, connected component labelling method, and morphological functions for breast extraction. Wavelet transform is used for enhancement of mammogram and triangular mask is used for pectoral muscle suppression. Morphological functions like opening, closing, erosion, dilation and reconstruction are used for the segmentation of mammogram to extract region of interest (ROI). From ROI, intensity histogram based texture features are extracted. Extracted features are fed into classifier algorithm. In this proposed work, concept of neural network is used for classification, which is applied for two levels. In the first level, neural network classify the segmented ROI into normal (without tumor) and abnormal (with tumor) ROI. Second level neural network classify abnormal ROI into malignant and benign masses. The proposed CAD system achieves 96.07% specificity and 94.73% sensitivity at first level classification, 91.66% specificity and 80% sensitivity at second level.
International Journal of Multimedia and Ubiquitous Engineering, 2017
This paper works on the detection of the breast cancer at early stage, by utilizing the mammogram images. This work pre-processes the given image by using histogram equalization to enhance the contrast of the image. Then the grey level co-occurrence matrix is used to extract the features from the image. The extracted features are reduced to the significant subset of features by using the sequential backward selection. Then, the image is classified as malignant or benign on the basis of significant subset of features by using the ANN classifier. Moreover, ANN classifier is optimized by selecting the optimized error value as stopping criteria. The result comparison and analysis on DDSM and MIAS datasets using parameters sensitivity, specificity, accuracy signifies effectiveness of the work.
Proceedings of the EURO-SIAM: European Conference for the Applied Mathematics and Informatics, Athens, Greece., 2010
Artificial Neural Network (ANN) techniques are increasingly being applied in many areas of medical fields for the analysis of complex data. In this paper, a new morphological approach based ANN is developed for an efficient detection of breast cancer from mammograms. The developed scheme consists of three steps: 1) a preprocessing step using traditional image enhancement techniques, 2) a proposed morphological segmentation algorithm and, 3) ANN using some extracted features is being applied for possible detection of abnormal cancer cells. The feasibility of the proposed approach was explored on 32 commonly virulent images provided by the Medical City Hospital in Jordan. The obtained results are encouraging; performing well in segmenting breast medical images and often generates a favorable detection of breast cancer from mammograms.
TJPRC, 2013
In this paper we present a technique to detect masses from digital mammograms using Artificial Neural Network (ANN), which performs malignant-normal classification on region of interest (ROI) that contains mass. The major mammographic characteristics for mass classification are Intensity, Shape and Texture. ANN exploits all such type of important factor to classify the mass into malignant or normal. The features used in characterizing the masses are mean, standard deviation, skewness, area, perimeter, homogeneity, energy, contrast and entropy. The main aim of the method is to increase the effectiveness and accuracy of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. ANN with nine features was proposed for classifying the marked regions into malignant and normal. With ANN classifier, experiment result shows the 96.875% accuracy, 96.551% sensitivity and 97.142% specificity.
Artificial neural network has been broadly utilized as a part of different fields as a wise instrument as of late, for example, artificial knowledge, design acknowledgment, medicinal determination, machine learning et cetera. The characterization of breast disease is a therapeutic application that represents an awesome test for analysts and researchers. As of late, the neural network has turned into a well-known device in the order of malignancy datasets. Significant detriments of artificial neural network (ANN) classifier are because of its slow merging and continually being caught at the neighborhood minima. In this paper we proposed a Nobel method for finding the breast cancer in the patent. We have used artificial neural network to classify the disease. Our proposed mechanism effectively classify the cancerous and non-cancerous mammogram of the female breast.
International Journal of Computer …, 2011
. The results show that the GLCM at 0 o , 45 o , 90 o and 135 o with a window size of 8X8 produces informative features to classify between masses and non-masses. Our method was able to achieve an accuracy of 91.67% sensitivity and 84.17% specificity which is comparable to what has been reported using the state-of-the-art Computer-Aided Detection system.
Computational and Mathematical Methods in Medicine
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We di...
2016
Artificial neural network has been a widely used tool in various fields of medical and engineering applications as an intelligent tool, such as artificial intelligence, pattern recognition, medical diagnosis, machine learning and many more. Breast cancer classification is medical applications that possess great challenge for scientists and researchers. Neural network has become a very popular tool in diagnosis of breast cancer and classification of cancer datasets. Breast Cancer is one of the fatal diseases causing more number of deaths in women. For early and efficient diagnosis of breast cancer more and more techniques are being developed. Classical methods required cytopathologists or oncologists to examine the breast lesions for detection and classification of various stages of the cancer. Artificial neural network is a branch of artificial intelligence and has been widely accepted as a new technology in computer science. In carcinogenesis, artificial neural networks have been s...
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Breast cancer is one of the leading causes of women fatalities. Breast cancer is a worldwide disease with symptoms and features being similar in almost all regions. In India, in the past 25-30 years there has been a steep increase in cases of breast cancer being reported in a comparatively younger age group (40+). Indian women have a higher rate of fatality due to breast cancer compared to the global average
2017
Now a days death rate of women due to breast cancer is very high, the reason behind this is detection of tumor in last stages. Various researches have proved that treatment of breast cancer is only possible when it is detected at a very early stage. One of the effective method for early detection is mammogram screening but reading of mammogram is not a very easy task so an algorithm is required which help radiologists. Therefore aim of this paper is to develop an algorithm which help radiologists by automatically detect suspicious region in mammogram and when suspicious region is detected we use artificial neural network (ANN) classifiers to classify masses whether it is normal or abnormal. The efficiency of this method is checked on 322 MIAS database. A sensitivity of 91.5% with only 0.1296 average false alarm per image is observed.
2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW), 2020
Now-a-days Breast cancer is one of the serious issues for women when compared to all other cancers. In India Breast cancer risk revealed that 1 in 28 women cause to grow and become more mature breast cancer during her lifetime. Breast cancer begins when cells in the breast use to grow or start to grow out of control and it advances from breast tissue. These cells usually form a tumor that can often be seen on a mammogram or felt as a lump. If cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body then the tumor is malignant(cancer). Breast cancer occurs in both women and men. In India the average age is the very great extent of cancer in the age group of 43–46 years unlike in the Western where women aged 53–57 years are more likely to suffer from breast cancer. This paper includes uses of various Data Mining along with neural networks to identify the presence of breast cancer at early stages and diagnose it efficiently.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.