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IEEE/ICACT20250193 Slide.08        [Big Slide]       [YouTube]	Oral Presentation
	Complete Workflow The skin cancer classification workflow starts with acquiring and preprocessing skin lesion images (resized to 224x224 pixels) using techniques like noise filtering, flipping, and rotation. The dataset is split for training and testing, with transfer learning models (Inception, Xception, VGG19, ResNet50) fine-tuned for classification. An ensemble approach combines model predictions to improve accuracy, while metrics like precision, recall, and F1 score evaluate performance. This workflow tackles data scarcity, class imbalance, and overfitting, ensuring robust skin cancer detection.

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