Abstract: Early and accurate classification of skin lesions is critical for effective skin cancer diagnosis and treatment. However, the visual similarity of lesions in their early stages often leads to misdiagnoses and delayed interventions. This lack of transparency makes it challenging for dermatologists to interpret with validate decisions made by such methods, reducing their trust in the system. To overcome these complications, Skin Lesions Classification in Dermoscopic Images using Optimized Dynamic Graph Convolutional Recurrent Imputation Network (SLCDI-DGCRIN-RBBMOA) is proposed. The input image is pre-processed utilizing Confidence Partitioning Sampling Filtering (CPSF) to remove noise, resize, and enhance image quality. By using the Hybrid Dual Attention-guided Efficient Transformer and UNet 3+ (HDAETUNet3+) it segment ROI region of the preprocessed dermoscopic images. Finally, segmented images are fed to Dynamic Graph Convolutional Recurrent Imputation Network (DGCRIN) for classifying skin lesion as actinic keratosis, dermatofibroma, basal cell carcinoma, squamous cell carcinoma, benign keratosis, vascular lesion, melanocytic nevus, and melanoma. Generally, DGCRIN does not express any adaption of optimization strategies for determining optimal parameters to exact skin lesion classification. Hence, Red Billed Blue Magpie Optimization Algorithm (RBBMOA) is proposed to enhance DGCRIN that can exactly classify type of skin lesion. The proposed SLCDI-DGCRIN-RBBMOA technique attains 26.36%, 20.69% and 30.29% higher accuracy, 19.12%, 28.32%, and 27.84% higher precision, 12.04%, 13.45% and 22.80% higher recall and 20.47%, 16.34%, and 20.50% higher specificity compared with existing methods such as a deep learning method dependent on explainable artificial intelligence for skin lesion classification (DNN-EAI-SLC), multiclass skin lesion classification utilizing deep learning networks optimal information fusion (MSLC-CNN-OIF), and classification of skin cancer from dermoscopic images utilizing deep neural network architectures (CSC-DI-DCNN) respectively.

J. Deepa and P. Madhavan, “Optimized Dynamic Graph-Based Framework for Skin Lesion Classification in Dermoscopic Images” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602115

@article{Deepa2025,
title = {Optimized Dynamic Graph-Based Framework for Skin Lesion Classification in Dermoscopic Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602115},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602115},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {J. Deepa and P. Madhavan}
}