Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3597 2018 2018 Ministry of Education, Wasit Education Directorate, Iraq Oday Oday Ministry of Education, Wasit Education Directorate, Iraq Fatima Hameed Shnan Computer Science and Information Technology, University of Wasit, Al Kut 52001, Iraq Huda Lafta Majeed Ministry of Education, Wasit Education Directorate, Iraq Oday Ali Hassen Medical imaging performs a critical position in modern healthcare, in particular in the early detection of cancers, which considerably enhances survival charges and treatment consequences. This study investigates a hybrid version combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to optimize medical image analysis. Leveraging advanced deep gaining knowledge of strategies along with Transfer Learning and Data Augmentation, the hybrid method validated advanced performance in class, segmentation, and anomaly detection obligations. Experimental results discovered that the hybrid version outperformed standalone CNN and ViT architectures, attaining high diagnostic accuracy whilst keeping computational efficiency. The findings spotlight the potential of AI-stronger answers to revolutionize clinical diagnostics by way of offering accurate and reliable computerized systems, paving the manner for broader medical programs and improved patient results. 2025 2025 117 127 10.54216/FPA.190111 https://www.americaspg.com/articleinfo/3/show/3597