CSEIT1836130 | Received : 15 August 2018 | Accepted : 30 August 2018 | July-August-2018 [ 3 (6) : 641-648 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 6 | ISSN : 2456-3307 641 Classification of Breast Lesions using Histopathology Images and Neural Network Sonali Nandish Manoli *1 , Anand Raj Ulle 1 , N.M Nandini 2 , T.S Rekha 2 * 1 Department of Information Science and Engineering, JSS Science &Technological University, Mysore, Karnataka, India 2 Department of Pathology, JSS Medical College affiliated to JSS University, Mysore, Karnataka, India ABSTRACT Breast cancer occurs when a malignant tumor originates in the breast. As breast tumors mature, they may metastasize to other parts of the body. However, it is important to keep in mind that, if identified and properly treated while still in its early stages, breast cancer can be cured [1].To achieve the above target it is necessary to develop a computer-aided Diagnosis system which helps in better diagnosis of the condition. It can be achieved by using Digital Image Processing techniques to obtain the regions of interest which show extra growth in the breast. So, a system is developed to classify lesions into Benign (non-cancerous) and Malignant (cancerous) condition. To classify the lesions the stain-color is considered as the important criteria to remove the noise from the digital images. To achieve this, initially the region of interest is obtained using k-means clustering and shape features are extracted. The binary image obtained as the result is further given as an input to obtain the regions of interest using the marker-controlled watershed image segmentation approach. The result of the hybrid approach gives us texture features. Further, the combination of these features is considered for classification. The performance measures namely accuracy , sensitivity , specificity , precision of the system are calculated for Naïve Bayes , Support Vector Machine , Adaptive Boosting , Classification Tree, Random Forest and Feed-Forward Neural Network Classifier. Keywords : Histopathology, Digital Images, Stain-Color Normalization, Stain-Color Deconvolution, Image Sharpening, K-means, Shape Features, Foreground Markers, Background Markers, Marker-Controlled Watershed, Texture Features, Classifier, Feed-Forward Neural Network. I. INTRODUCTION A breast lesion is an extra growth or lump formed on the breast. It modifies into cancer when there is growth of cancer cells in the tissues of breast. Hence there is a necessity to find the kind of lesion so that it can be treated accordingly by an oncologist [1].Breast Cancer occurrences are increasing every year, in India, for every two women newly diagnosed with breast cancer, one lady is dying of it[2].Digital Pathology is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor. It requires high quality scans free of dust, scratches, and other obstructions [3]. In medical field, to enhance the identification of the type of breast lesion there is use of Computer aided Diagnosis method so that the accuracy of classifying samples is enhanced and better treatment is given. The most effective method of classification include classification using scores like Bloom-Richardson