Breast Cancer Histopathological Image Classification Using CNN and VGG-19

Applications of deep learning in medicine, like iden- tifying the kind of malignant cells, are common. Breast cancer is a most common type of cancer in women and it a main cause of death for women. There are three categories for the malignant cells: Normal, Mild, and Severe. Early diagnosis of the malignant cells can prevent these deaths. Numerous techniques, including MRIs, mammograms, ultrasounds, and biopsies, are used to identify cancerous cells. Hematoxylin and eosin-stained breast cancer histology photos are difficult to diagnose, labor-intensive, and frequently cause pathologists to disagree. Recent advances in deep learning have made histological image processing possible with convolutional neural networks (CNNs). Histology images of breast cancer are categorized into sub-classes based on general tissue structure and morphology, as well as the density, variabil- ity, and organize of the cells. These subclasses include benign, malignant, and normal. Using this information, extract features at the cell and tissue levels, respectively from histopathological images, in smaller and larger size patches. The dataset repository is where the input image was obtained. The image has to be pre- processed. The feature extraction must then be put into practice. The pre- processed image must then be segmented. The image must be divided. We are able to apply many neural network models, including VGG-19 and Convolutional Neural Network (CNN). The findings of the experiment indicate that the accuracy. The primary goal of our method is to identify or anticipate breast cancer based on the input image.