SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

SIMPLE SUMMARY: Skin cancer is a major concern worldwide, and accurately identifying it is crucial for effective treatment. we propose a modified deep learning model called SBXception, based on the Xception network, to improve skin cancer classification. Using the HAM10000 dataset, consisting of 10,015 skin lesion images, the model achieved an impressive accuracy on a test set. SBXception also showed significant improvements, requiring fewer parameters and reducing training time compared to the original model. This study highlights the potential of modified deep learning models in enhancing skin cancer diagnosis, benefiting society by improving treatment outcomes. ABSTRACT: Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis. This study proposes SBXception: a shallower and broader variant of the Xception network. It uses Xception as the base model for skin cancer classification and increases its performance by reducing the depth and expanding the breadth of the architecture. We used the HAM10000 dataset, which contains 10,015 dermatoscopic images of skin lesions classified into seven categories, for training and testing the proposed model. Using the HAM10000 dataset, we fine-tuned the new model and reached an accuracy of 96.97% on a holdout test set. SBXception also achieved significant performance enhancement with 54.27% fewer training parameters and reduced training time compared to the base model. Our findings show that reducing and expanding the Xception model architecture can greatly improve its performance in skin cancer categorization.