Related Experiment Videos
An interpretable skin cancer detection and classification framework using meta-heuristic driven symmetric
Vijayakumar Reddy D1, Balaji L2
1Research Scholar, School of Electrical and Communication, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu.
Abstract:
Skin cancer requires early and accurate detection for effective treatment. Automated image analysis can help identify skin lesions and support clinical diagnosis.
Abstract:
This study aims to develop an efficient framework for accurate skin cancer detection using lesion segmentation, deep learning, and optimization.
Abstract:
Skin lesion images are first segmented using MobileUNet++ with Spatial Attention (MUNet++-SA) to identify lesion regions accurately. The segmented images are then classified using the proposed SAMNet-LSTM model. The model parameters are optimized using Rationalized Masterpiece Optimization (RMO) to improve classification performance. The proposed method is compared with existing approaches.
Abstract:
The proposed MUNet++-SA achieves improved segmentation performance. Its mean IoU is 16.47%, 15.88%, 14.11%, and 8.23% higher than U-Net, U-Net3+, ResUNet, and DenseUNet, respectively. The RMO-optimized SAMNet-LSTM also provides improved and efficient skin cancer classification.
Abstract:
The proposed framework combines accurate lesion segmentation with optimized deep learning for reliable skin cancer detection. It can support faster and more effective clinical decision-making.
