ENHANCING SKIN DISEASE DETECTION WITH OPTIMIZED VGG-19 AND EXPLAINABLE GRAD-CAM VISUALIZATION

Skin infections are a major concern for human health, as they can cause significant skin damage, leading to loss of confidence and emotional distress in patients. Advancements in deep learning offer promising solutions for diagnosing and treating such conditions effectively. AI-driven approaches enable automated skin disease detection without requiring expert intervention, making diagnosis more accessible. Enhancing the user interface of these systems can further improve user experience. Early identification of skin disorders is crucial in preventing misdiagnosis as minor allergies, which can otherwise lead to severe complications. This research explores the application of deep learning for improved skin infection detection and treatment. Leveraging the power of AI, the study introduces a novel classifier combining the VGG-19 convolutional neural network with Grad-CAM (Gradient-weighted Class Activation Mapping). This approach aims to enhance diagnostic accuracy and reduce the risk of misdiagnosis, ultimately minimizing patient complications. The model was trained and evaluated using a dataset sourced from Kaggle, a popular platform for machine learning datasets. Performance was compared against baseline machine learning models, including decision trees and Support Vector Machines (SVMs). Results indicate that the proposed dual-input model, incorporating VGG-19 and Grad-CAM, achieved a remarkable accuracy of approximately 96%. This significantly outperforms the baseline models, demonstrating the potential of deep learning techniques for accurate and efficient skin condition diagnosis. The improved performance suggests that this approach could be a valuable tool for dermatologists and other medical professionals in the future.