Exploring Deep Learning Approaches for Multimodal Breast Cancer Dataset Classification and Detection

Ahmed A.F Osman, Rajit Nair, Sultan Ahmad, Mosleh Hmoud Al-Adhaileh, Ramgopal Kashyap, Hikmat A. M. Abdeljaber, Sami A. Morsi and Rami Taha Shehab

Data and Metadata, 2025, vol. 4, 1136

Abstract: Introduction; Globally, we need advanced testing to detect breast cancer early. New breast cancer diagnosis methods using mixed datasets and deep learning promise improved accuracy. Objective; These sets, which comprise several imaging modalities, show tumor characteristics well. VGG16, AlexNet, and ResNet50 are effective deep learning models in many domains, yet their breast cancer diagnosis performance is unclear. Method; This paper examines these patterns' benefits, downsides, and research gaps. We also provide two novel approaches, Attention-based Multimodal Fusion (AMF) and Improved Generative Adversarial Augmentation (GAA), to improve deep learning models on breast cancer datasets. Result; The findings highlight the potential of machine learning to show tumor characteristics well. Conclusion; We prove that our breast cancer screening technologies are the most accurate and dependable via extensive testing.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:1136:id:1056294dm20251136

DOI: 10.56294/dm20251136

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