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IEEE/ICACT20250193 Slide.03        [Big Slide]	Oral Presentation
	Introduction: - Skin cancer is one of the most common forms of cancer worldwide, with early detection being critical for improving patient outcomes and survival rates. Conventional diagnostic methods rely heavily on clinical expertise, which can be subjective and prone to variability, leading to potential misdiagnosis. Transfer learning models leverage pre-trained neural networks to efficiently analyze medical images, reducing the need for extensive datasets and improving diagnostic accuracy. Combining multiple models through an ensemble approach boosts predictive performance, increases reliability, and minimizes errors in skin cancer detection.

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