Aug 2026· The Arabian journal for science and engineering· Vol 51, pp. 18645 - 18661· 0 citations· 45 references
TL;DR
A hybrid approach that integrates convolutions with linear projections is introduced, combining the ability of convolutions to effectively capture local spatial features with the capacity of linear projections to model global relationships, leading to a more expressive and robust feature representation.
A reliable multimodal deep learning framework that integrates dermoscopic images, 3D Total Body Photography scans, and patient clinical data to enhance diagnostic accuracy in skin cancer classification and uses an Artificial Neural Network architecture for clinical data classification is presented.
Uttam Mittal, S. Varpe, A. Sharma et al.· IEEE Access· 0 citations
A novel, robust, and efficient hybrid model that combines lightweight CNNs and ViTs to enhance the accuracy and reliability of automated skin lesion segmentation for use in diverse clinical settings is developed.
Xian-Hong Wang, Muhammad Saeed, Naeem Ahmed et al.· Frontiers in Medicine· 0 citations
A CNN–Transformer-based framework with latent bottleneck learning for robust multi-class skin cancer classification is proposed, which offers compact, interpretable and generalizable representations for reliable dermatology decision support in heterogeneous imaging conditions.
The proposed framework effectively integrates novel preprocessing, segmentation, hybrid feature representation, feature optimization, and classification strategies to improve the robustness and accuracy of automated skin cancer classification and demonstrates its potential to support reliable computer-aided diagnosis a...
The proposed framework offered a valid, meaningful and clinically relevant solution for automated melanoma screening and revealed high sensitivity to the detection of malignant lesions, as 154 out of 167 images of melanomas were correctly identified.
Ashish Jain, Rashmi Yadav· Natural Resources for Human...· 0 citations
The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.
A. Star, Gibi Linza, Siva Durshika et al.· 0 citations
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