Automated Detection and Classification of Breast Cancer Tumour Cells using Machine Learning and Deep Learning on Histopathological Images

Cancer is the result of abnormal growth of cells in a specific body part. It can outspread to other body parts rapidly if not diagnosed in a timely manner. Breast cancer is caused due to development of cancer cells in the breast tissue of women. Breast Cancer is the most frequent cause of death in women after lung cancer. If detected at primary stages, the breast cancer can be cured and the chances of survival drastically increases. Advances in screening and treatment for breast cancer have improved survival rates dramatically since 1989[1]. In this paper we have applied machine learning for image classification and further segmentation algorithms is applied for detection of the tumorous cells. The designing of the model began with classification of histopathological image dataset into Cancerous and Non - cancerous classes using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms. Both the classifiers are examined on the basis of sensitivity, specificity, accuracy, precision, f1-score parameters. The resulting image i.e., Cancerous obtained from the classification algorithms are further used as an input for Image segmentation models. Genetic Algorithm (GA) and K-Means are used for the segmentation of the histopathological images. Experimental results showed that CNN for image classification in combination with GA for image segmentation gave more precise results with accuracy 99%.