Enhanced Skin Cancer Diagnosis via Optimized ResNet150v2-CNN Integration

Precise classification and detection of skin lesions are critical for enabling timely diagnoses and effective treatments. This project leverages the comprehensive HAM10000 dataset, which features a wide range of annotated skin lesion images, to develop an innovative hybrid model for lesion classification. By integrating convolutional neural networks (CNNs) with traditional machine learning techniques, alongside sophisticated data augmentation strategies, we enhance model robustness and accuracy while mitigating overfitting issues. Our approach emphasizes transfer learning to exploit pretrained networks, facilitating more effective feature extraction from complex visual patterns within the dataset. Comprehensive evaluation metrics which includes specificity, sensitivity, precision, accuracy and F1-score, reveal that Hybrid-Model significantly outperforms traditional methods, achieving 97% of accuracy and 97.23% of F-Score. In dermatology, this research not only advances automated skin lesion analysis but also highlights the transformative potential of artificial intelligence in dermatology. Ultimately, our findings aim to enhance diagnostic accuracy, thereby promoting early skin cancer detection and fostering personalized patient care pathways.