A pruned and parameter-efficient Xception framework for skin cancer classification.

Skin cancer is one of the most prevalent and potentially lethal diseases worldwide, with early detection being critical for patient survival. This study presents a novel framework that leverages transfer learning, pruning, SMOTE, data augmentation, and the advanced Avg-TopK pooling method to improve the accuracy and efficiency of skin cancer classification using dermoscopic images. The HAM10000 dataset was used to evaluate the performance of various transfer learning models, with Xception as the top performer. A layer-based pruning strategy was proposed to optimize the model and reduce its complexity. SMOTE and data augmentation were applied to address the class imbalance within the dataset, significantly improving the model's generalization across all skin lesion classes. The utilization of the Avg-TopK pooling technique further enhanced model accuracy by preserving crucial image features during the downsampling process. The proposed approach achieved an overall accuracy of 91.52%, surpassing several state-of-the-art models. Following pruning, the model's parameter count was reduced by approximately 35%, from 20.9 million to 13.5 million, improving efficiency and performance. This framework demonstrates the effectiveness of combining model pruning, oversampling, and advanced pooling methods to build robust and efficient skin cancer classification systems suitable for clinical applications.