aspg-3-3597-1791417673 20261008000113 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 1 Bushra Majeed Muter Ministry of Education, Wasit Education Directorate, Iraq Fatima Hameed Shnan Ministry of Education, Wasit Education Directorate, Iraq Huda Lafta Majeed Computer Science and Information Technology, University of Wasit, Al Kut 52001, Iraq Oday Ali Hassen Ministry of Education, Wasit Education Directorate, Iraq 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 117 127 3597 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190111 https://www.americaspg.com/journal/3/article/3597 https://www.americaspg.com/storage/11739927023.pdf