Skin Lesion Classification Using Deep Learning Technique

Classifying skin lesions, particularly melanoma, remains a significant challenge due to low contrast, high noise, and visual similarities among lesions. This research proposes an ensemble model combining Inception V3, DenseNet201, and EfficientNet B7, leveraging their unique strengths in capturing complex features from dermatoscopic images. By employing transfer learning with pre-trained models and fine-tuning on a melanoma dataset, the ensemble achieves remarkable accuracy, surpassing previous benchmarks. Specifically, the models attain accuracy rates of 89.9% (InceptionV3), 92.2% (DenseNet201), and 91.2% (EfficientNet B7). This study represents a critical step toward developing robust and efficient computer-aided melanoma classification systems for clinical use, though further clinical validation is needed.