A Multi-Modality Approach to Breast Cancer Diagnosis: Fusing Ultrasound with Other Imaging Techniques

It is the most crucial health issue related to mortality rates among females globally. In relation to that, there have been recent developments in AI and ML to serve better the diagnosis process of breast cancer. The present study hence forth proposed a new way of analzsing the breast using ultrasound imaging with transfer learning and ensemble methods. It integrates cutting edge transfer learning models with advanced ensemble techniques based on Multi-Layer Perceptron’s and Support Vector Machines with different kernels. Finally, this system is evaluated, which provides a classification accuracy of 90% and an overall accuracy of 90% with evidence of significant i m provement o v er existing diagnostic systems. It offers a structured procedure covering all steps from handling the input data up to model training and performance evaluation; it, therefore seems to be particularly effective for diagnostics enhancement related to breast cancer.