ArticleSensors (Basel, Switzerland)2022
An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata.Frontiers in medicine · 2026Article
- Integrating anisotropic heat flow and transformer encoders in convolutional neural network for skin cancer classification.Frontiers in medicine · 2026Article
- Article
- DermaMamba: A Dual-Branch Vision Mamba Architecture with Linear Complexity for Efficient Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images.Frontiers in medicine · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- Enhanced MobileNet for skin cancer image classification with fused spatial channel attention mechanism.Scientific reports · 2024Article
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Detection and classification of surface defects on hot-rolled steel using vision transformers.Heliyon · 2024Article
- MLASM: Machine learning based prediction of anticancer small molecules.Molecular diversity · 2024Article
- Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.Journal of clinical medicine · 2024Article
- An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks.Diagnostics (Basel, Switzerland) · 2024Article
- Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.Archives of dermatological research · 2024Review
- Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.PeerJ. Computer science · 2024Article
- Classification of hyper-scale multimodal imaging datasets.PLOS digital health · 2023Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Transformer-Based Approach to Melanoma Detection.Sensors (Basel, Switzerland) · 2023Article
- Identifying the role of vision transformer for skin cancer-A scoping review.Frontiers in artificial intelligence · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases. However, there are certain challenges associated with the accurate classification of these diseases such as a lower detection accuracy, poor generalization of the models, and an insufficient amount of labeled data for training. To address these challenges, in this work we developed a two-tier framework for the accurate classification of SC. During the first stage of the framework, we applied different methods for data augmentation to increase the number of image samples for effective training. As part of the second tier of the framework, taking into consideration the promising performance of the Medical Vision Transformer (MVT) in the analysis of medical images, we developed an MVT-based classification model for SC. This MVT splits the input image into image patches and then feeds these patches to the transformer in a sequence structure, like word embedding. Finally, Multi-Layer Perceptron (MLP) is used to classify the input image into the corresponding class. Based on the experimental results achieved on the Human Against Machine (HAM10000) datasets, we concluded that the proposed MVT-based model achieves better results than current state-of-the-art techniques for SC classification.
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