A Deep Learning Approach for the Automatic Analysis and Prediction of Breast Cancer for Histopathological Images using A Webapp
Mohammed Abdul Raheem, Shaik. Sadiya Tabassum, Syeda Kulsoom Nahid, Syeda Areej Anzer · Zenodo (CERN European Organization for Nuclear Research) · 2021
Since time immemorial, women are the victims of breast cancer which is a predominant disease worldwide with high dreariness and mortality. The absence of careful visualization models brings about trouble for specialists to set up a treatment plan that may lengthen outpatient endurance time. More often than not, Breast Malignancy is distinguished by utilizing a biopsy strategy where the tissue is taken out and examined under a magnifying lens. In the event that a histopathologist isn't very much trained person, at that point this may lead to some wrong findings. And due to this one may have to undergo wrong diagnosis. To encourage better diagnosis, the programmed examination of histopathology images can assist pathologists with recognizing harmful tumors and malignancy subtypes. Convolutional Neural Networks have become favored Deep Learning approaches for computer vision tasks which involves feature extraction and image classification. This proposed work focuses on building a machine learning model that can classify histopathology images into two classes namely cancer (malignant) and non-cancer (benign) using transfer learning approach. To make our work beneficial to everyone, we made a website that is being built and deployed using Streamlit library, where we integrated the model with highest accuracy for classifying the histopathology images. Our work will help doctors and medical practitioners for early diagnosis of breast cancer.