Evaluating the Performance of a Fine-Tuned Xception Network for Skin Cancer Classification

Still a major global health issue, skin cancer depends mostly on early and correct diagnosis to lower mortality rates. For medical image analysis, especially with regard to skin cancer classification, deep learning has become a transforming tool. In this work, we present an Xception-based fine-tuned deep learning model to categorize multiclass skin lesions. By means of architectural changes and transfer learning, the model was tuned to extract strong features and enhance generalization over a heterogeneous dataset. With cutting-edge methods including Swish activation, dropout regularization, and batch normalization, the network attained an accuracy of 88%. We examined training and validation performance to assess the model and observed a consistent drop in training loss while preserving a constant validation loss, hence indicating efficient learning and lower overfit. Comparative experiments demonstrated the efficacy of the fine-tuned Xception model using currently used hybrid and ensemble-based techniques such RvXmBlendNet and IncepX-Ensemble Model. Moreover, dropout layers significantly improved the generalizing ability of the network, hence addressing usual overfitting in medical data sets. This work advances automated diagnosis systems by showing the possibilities of transfer learning and optimized architectures in skin cancer detection. The suggested model presents a good tool for helping doctors classify and early detect skin lesions.