2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 1 , PP: 117-127, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Bushra Majeed Muter 1 , Fatima Hameed Shnan 2 , Huda Lafta Majeed 3 , Oday Ali Hassen 4 *
Doi: https://doi.org/10.54216/FPA.190111
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.
Medical Imaging (MI) , Cancer Detection , Deep Learning (DL) , Vision Transformers (ViTs) , Convolutional Neural Networks (CNN) , Transfer Learning (TL) , Data Augmentation (DT) , Hybrid Model
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