Classification of Breast Cancer Histopathology Images using Machine Learning Algorithms

Abstract

Machine Learning (ML), provides system the capacity to learn instinctively and allows systems to improve themselves with past experience and without being programmed specifically. In the field of Medical Science, ML plays important role. ML is being used to develop new practices in medical science which deals with huge patient data. Breast Cancer is a chronic disease commonly diagnosed in women. According to the survey by WHO, rank of breast cancer is at number one as compared to other cancers in female. BC has two kinds of tumour: Benign Tumour (BT), and Malignant Tumour (MT). BTs are treated as non-cancerous cells. MTs are treated as cancerous cells. The unidentified MTs in time stretch to other organs. Treatment procedure for BT and MT is different. So, it is salient to determine precisely whether a tumour is BT or MT. In this proposed model, Histopathology Images are used as dataset. These Histopathology images are pre-processed using Gaussian Blur and K-means Segmentation. The pre-processed data fed into feature extraction model. ML algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Convolution Neural Network (CNN) are applied to extracted features. Performance of these algorithms is analysed using accuracy, precision, recall and F1-score. CNN gives the highest accuracy with 87%.