A Multimodal Approach Toward Detection and Identification of Breast Cancer

Detection and diagnosis of breast cancer are considered one of the significant challenges in the field of medical imaging. Accurate and reliable methods for early identification are necessary to improve outcomes. The paper presents a multimodal deep learning (DL) framework toward the detection and identification of breast cancer, which makes use of multiple imaging modalities: mammography, biopsy, and ultrasound. The approach provided has been an integration of feature extraction techniques from the imaging sources to provide a comprehensive analysis of suspected breast lesions. The paper is designed to classify cases as either benign or malignant through state-of-the-art DL algorithms that integrate CNNs and ensemble learning techniques. Promising results are shown with the method by integrating various imaging modalities toward improved diagnostic performance. The work will serve as a foundation for an integrated, multimodal approach to breast cancer diagnosis, empowering clinicians with the powerful tool for the intervention and tailored treatment planning.