ArticleCancers2023
SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- An attention based optimized network for the classification of skin lesions.Scientific reports · 2026Article
- GSARC: a group shuffle activated redundant connection framework for post-hoc feature space adaptation in skin lesion classification on the HAM10000 dataset.Frontiers in artificial intelligence · 2026Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms.Diagnostics (Basel, Switzerland) · 2025Article
- Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.Frontiers in microbiology · 2025Article
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Next-generation approach to skin disorder prediction employing hybrid deep transfer learning.Frontiers in big data · 2025Article
- A transfer learning enabled approach for ocular disease detection and classification.Health information science and systems · 2024Article
- Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system.Scientific reports · 2024Article
- Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Development, Application and Utility of a Machine Learning Approach for Melanoma and Non-Melanoma Lesion Classification Using Counting Box Fractal Dimension.Diagnostics (Basel, Switzerland) · 2024Article
- Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.Scientific reports · 2024Article
- Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System.International journal of biomedical imaging · 2024Article
- Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.Diagnostics (Basel, Switzerland) · 2023Article
- MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor.Journal of imaging · 2023Article
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.