Classification of Breast Cancer Tumors from Histopathological Images through a Modified ResNet-50 Architecture

Abstract

The diagnosis of malignant or benign breast cancer tumors from histopathological images is challenging due to human error, which may lead to the patient undergoing additional, often painful, procedures to collect new data. Utilizing a supervised, pre-trained ResNet-50 model as a second opinion for doctors can help eliminate the need for repeated procedures. One main challenge faced by doctors and machine learning models is image blurriness. Applying various data preprocessing and augmentation techniques, such as resizing, Gaussian blurring, histogram equalization, and color space conversions, can improve the model's performance. The model achieved its best results with an accuracy of 95.61%, precision of 96%, recall of 94%, and an F1-score of 95%.