Deep Learning for Predicting Invasive Ductal Carcinoma in Histopathological Tissue Images

Nearly 80% of instances of breast cancer are invasive ductal carcinoma (IDC), the most prevalent and aggressive kind of the disease. Improving patient outcomes requires early detection and accurate diagnosis, but conventional diagnostic techniques rely on pathologists’ manual tissue analysis, which is labour-intensive and subject to error. In order to improve diagnostic efficiency and accuracy, this study investigates the use of sophisticated convolutional neural networks (CNN to automate IDC identification and categorisation in histopathology pictures. In contrast to traditional methods, our method optimises extraction of features and classification by integrating a hybrid neural networks architecture that combines transfer learning and attention mechanisms. Our approach classifies tissues patches into three groups: healthy, IDC, as well as various types of breast cancer subtypes, using a dataset of samples of tissues from 162 IDC patients. To enhance generalisation over a range of histopathological abnormalities, the suggested method makes use of a refined CNN architecture, automatic data augmentation, and intensive preprocessing. Our approach performs better than conventional models, providing increased reliability in cancer detection, according to the performance assessment using F1-score with equal accuracy. This method makes use of cutting-edge deep learning developments to produce a more effective and precise diagnostic tool, which is especially advantageous in clinical settings with limited resources.