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IEEE/ICACT20250100 Slide.05        [Big Slide]	Oral Presentation
	To enhance feature extraction, we used a modified ResNet50V2 model that included Squeeze-and-Excitation (SE) architecture. By recalibrating channel-wise feature responses, SE Module improves CNNs' representational capability by enabling the model to suppress less pertinent features and concentrate on more useful ones adding security Our updated ResNet50V2 model performed exceptionally well, achieving 99.97% training accuracy and 97.98% validation accuracy. These outcomes show that the proposed method has the potential to be used practically in clinical settings where patient care depends on high-precision

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