Accurate lesion segmentation remains a critical yet challenging task in medical imaging due to scarce pixel-level annotations, inter-observer variability and modality-specific noise. Conventional fully supervised models require exhaustive manual labeling, which limits scalability and clinical applicability. To address...
A. Zaheer, Sui-Rong Cao, Muhammad Farhan et al.· IEEE journal of biomedical a...· 0 citations
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...
A multimodal breast cancer diagnosis model is presented that consists of attention-based transformers to achieve efficient modality specific feature extraction, the modified mantissa search algorithm to remove irrelevant features, and the American zebra optimization algorithm to dynamically and efficiently combine feat...
Varun Malik, Tahani Alsubait, Mudassir Khan et al.· Frontiers in Medicine· 0 citations
The proposed framework integrates attention-enhanced recurrent modelling with physically informed heuristic constraints with physically informed heuristic constraints, allowing the model to capture complex nonlinear atmospheric dynamics while maintaining meteorological consistency.
Kalyan Chatterjee, Mudassir Khan, Bhoomeshwar Bala et al.· Scientific Reports· 0 citations
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