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Skin cancer is the most prevalent cancer globally, often misdiagnosed due to its similarity to benign conditions. The urgency for accurate diagnostic tools is underscored by the potential for improved patient outcomes through early detection. A study led by Aliyu Tetengi Ibrahim at Ahmadu Bello University introduces an AI model that categorizes skin lesions into seven types, significantly enhancing diagnostic accuracy.
The AI model utilizes advanced techniques like transfer learning and test time augmentation, achieving a remarkable accuracy rate of 94.49%. This innovation not only reduces unnecessary biopsies but also promotes timely interventions, potentially saving lives. The integration of this technology into telemedicine could democratize access to skin cancer diagnostics, especially in underserved regions.
• AI model categorizes skin lesions into seven distinct categories.
• Achieved 94.49% accuracy using advanced AI techniques.
Transfer learning allows the model to leverage knowledge from pre-trained models to improve classification accuracy.
TTA enhances the dataset by applying random modifications to test images, improving the model's generalization.
Deep learning techniques are employed to develop a sophisticated model for skin cancer classification.
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