Improving Classification Accuracy on Breast Histopathology Images Dataset Using Transfer Learning

At the present time, one of the main causes of mortality for women is Breast Cancer (BC). The pathologist still faces several difficulties in accurately diagnosing cancer. Invasive Ductal Carcinoma (IDC), the common kind of BC, has been categorized in this study. Numerous innovative strategies have been used in the realm of medical research for the categorization of IDC. However, there are a number of issues with the BC classification approach, including vanishing gradient, class imbalance, data overfitting, low accuracy rate, and latency to discover cancer cells in patients. Therefore, creating a precise and well-structured method for IDC categorization is essential. In order to address these issues, a productive technique has been put out, in which the classification model is specified as the TransResCNN model, or transfer learning applied to the CNN model of the pre-trained residualnetwork (ResNet). The most widely used techniques for handling large datasets are transfer learning and data augmentation. In order to assess the model's performance, an image-based confusion matrix is used to classify IDC. A number of assessment criteria, including recall, F1-score, accuracy, and precision, have also been used. Upon comparing our suggested research with other current studies, it was found to have the greatest accuracy (91.66%) and F1-score (94.22%). The examined study demonstrates that, in comparison to earlier research investigations, our suggested technique produced better results.