Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2

• Published in: 2023 4th International Conference on Intelligent Technologies (CONIT) pp. 1 - 7
• Main Authors: Kumar, M Ranjith, Priyanga, S, Anusha, Janani Srinivasan, Chatiyode, Veda, Santiago, Jerome, Revathi, P
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.06.2024
• Subjects: Accuracy; Computational modeling; Computer architecture; Computer vision; Deep learning; Dermatological Diagnostics; MobileNetV2; Skin cancer classification; Transformers; Transforms; Vision Transformer
• ISBN: 9798350349887
• DOI: 10.1109/CONIT61985.2024.10627580

Skin cancer is one of the most common types of cancer in the world, and it poses major health risks due to its ability to spread quickly and metastasize. Early and accurate identification is crucial for treatment success and improved patient outcomes. This proposed work combines the MobileNetV2 and Vision Transformer (ViT) architectures to create a hybrid automated skin cancer classification technique. This technique aims to increase the accuracy and efficiency of dermatological diagnosis tools by combining MobileNetV2's effective feature extraction capabilities with ViT's self-attention mechanism. After testing on the HAM10000 dataset, this hybrid model outperformed individual models with a remarkable 96.3 \% classification accuracy. Not only did the integration of ViT and MobileNetV2 improve the classification accuracy but it also demonstrated how various deep-learning architectures work together to tackle complex image analysis tasks. Classifying skin cancers is critical to the medical industry because it allows for the early diagnosis of various dermatological disorders, allowing prompt intervention and treatment. These benefits can eventually improve patient outcomes and save lives in huge numbers. The findings of this research highlight the potential of deep learning to transform dermatological diagnostics and open the door to creating systems that will significantly impact clinical practice by detecting skin cancer with greater accuracy and efficiency.