Leveraging Machine Learning to Tailor Breast Cancer Treatment Plans

Abstract

Breast cancer is a major cause of cancer mortality in women and therefore early and precise detection is essential in treatment. The traditional diagnostic techniques like mammography and histopathology are still popular and are constrained by the variability of the observer and sensitivity in complicated cases. Convolutional Neural Networks (CNNs) Deep learning has become a potent remedy, being able to extract features automatically with high accuracy on classification. Nevertheless, there are still issues in small data sets, binary only classification, and low interpretability. This paper will suggest a hybrid architecture combining CNNs and Capsule Network to enhance histopathologybased breast cancer detection. The results have been assessed using 6,000 images preprocessed and tested on various architectures, which are EfficientNetB0, InceptionV3, ResNet variants, and MobileNet V2. Among them, CapsuleNet model had the highest accurate, precise, recall and F1-score of 96.44 that surpassed any baselines tested. Gradient-weighted Class Activation Mapping (Grad-CAM) was also used to make the results more interpretable, giving heatmaps that show which parts of the image affected the models in the results, and this particular aspect should give the results more transparency in clinical practice. To be implemented practically, the optimized CapsuleNet model was translated to a mobile-based application with the help of TensorFlow Lite. This provides accessibility in resource constrained healthcare settings and proves the possibility of integrating in real world settings. Since it integrates good diagnostics and explainability and portability, the proposed solution can be useful in academic studies and clinical practice, helping to diagnose diseases earlier, minimize false diagnosis, and lead to better patient outcomes.