Breast Cancer Detection using Artificial Neural Networks

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.

Computer Optics, 2021

Breast cancer is a leading cause of death in women due to cancer. According to WHO in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25-30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thre...

International Journal of Electrical and Computer Engineering (IJECE), 2021

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women's lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computeraided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%.

2013

The high number of exams that is done in healthcare institutions increases the medical doctors' workload, leading to poor working conditions and the increase of wrong diagnoses. As consequence, an automatic system that can help medical doctors in diagnostic tasks is of major interest to any healthcare institution. The chapter proposes an Image Based Classification Platform suitable to help Medical Doctors diagnosing breast cancer, based on mammograms, i.e., to detect if a tumor is present in the image. The platform is twofold, i.e., in the first part the image descriptors are extracted from the image using image-processing algorithms. The obtained descriptors are used in the second part. The second part is related to classification, where computational intelligence methods are used to classify a given image, based on the descriptors obtained in the first phase. Texture analysis based on co-occurrence matrices are applied to obtain the descriptors from the MIAS database of mammograms. From these descriptors, fuzzy models, neural networks, and support vector machines are successfully used to classify the mammograms and obtain a diagnosis.

International journal of simulation: systems, science & technology, 2020

Breast cancer is one of the most common malignant diseases among women. Mammography is at present one of the available methods for early detection of abnormalities, which is related to breast cancer different lesions to show characteristics such as masses, which can be detected through this technique. The images are divided according to the Mini-MIAS database. In order to classify the cluster as malignant or benign, the 2nd order co-occurrence statistical properties of the image such as contrast, correlation, homogeneity, and energy were adopted. The gray-scale convergent matrices (GLCM) are used with a suggested feature of gray level density matrices (GLDM) to identify abnormal tissues (malignant) and natural tissue (benign). The proposed method of analysis is tested on several images (taken from a UK organization interested in understanding breast X-ray images) from the database of digital mammograms and outstanding results were obtained for breast cancer detection. The proposed multilayered design performance significantly improved the diagnosis of breast cancer by more than 95% and 95.8% sensitivity, 95.5% specificity for all datasets, 91.1% with 100% sensitivity and 84% specificity for 70% training data and 30% testing data.

2000

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it's infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

Applied Mathematical Sciences, 2013

We present an application of artificial neural networks to mammographic images, aimed at improving early detection of sensitivity to breast cancer. The proposed application consists of two main steps: a pretreatment step whose role is to extract the characteristics of the available mammographic images using the standard library OpenCV [1]; and a classification step based on an artificial neural network that uses these characteristics as input vectors for its training algorithm. The output of the training phase of this model is a categorization of the pretreated images into two main groups: normal and abnormal. After the training phase, the network can be used in order to label new and unseen images as normal or abnormal. We illustrate the performance of this model using a database of 322 real medical mammographic images.

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.

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.

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.

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...

CENTRAL ASIAN JOURNAL OF MEDICAL AND NATURAL SCIENCE, 2022

Among females, breast cancer is high as a major killer. Breast cancer is easily diagnosed when anomalies are spotted in their earliest stages. Accurately diagnosing breast cancer and treating patients as soon as possible will be facilitated by effective diagnostic technologies. Experiments were performed to determine if breast cancers were benign or malignant using data from the Wisconsin Diagnosis Breast Cancer database. To do this, we employ the supervised learning algorithm Support Vector Machine (SVM) with kernels such as Linear and Neural Networks (NN). Comparing the models' results reveals that the Neural Network technique is more "accurate" and "precise" than the Support Vector Machine in the categorization of breast cancer and appears to be a quick and efficient method.

2003

We propose an approach using artificial neural networks to classify masses in mammograms as malignant or benign. Single-layer and multi-layer perceptron networks were used in a study on perceptron topologies for pattern classification of breast masses. The boundaries of 108 breast masses and tumors were manually delineated and represented by polygonal models for shape analysis. Ribbons of pixels were extracted around the boundary of each mass. Three shape factor measures based on the contours, and fourteen texture features based on gray-level co-occurrence matrices of the pixels in the ribbons were computed. Various combinations of the features were used with perceptrons of several topologies for classification of benign masses and malignant tumors. The results were compared in terms of the area AZ under the receiver operating characteristics curve. Values of AZ up to 0.99 were obtained with the shape factors, whereas texture features provided Az up to only 0.63.

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