COMPARATIVE ANALYSIS OF DEEP LEARNING ALGORITHMS FOR AUTOMATED SKIN DISEASE DIAGNOSIS FROM DERMOSCOPIC IMAGES

Nazirova , Elmira, Abdusalomova , Shokhista

Innovation science and technologiy · 2026-yil

Annotatsiya

A comparative analysis of CNN, ResNet-50, and EfficientNet-B4 architectures for automated skindisease classification from dermoscopic images was conducted. The study was performed using the HAM10000dataset comprising 10,015 images across seven nosological classes. Model performance was evaluated usingaccuracy, sensitivity, specificity, F1-score, AUC-ROC, inference time, and memory footprint. EfficientNet-B4demonstrated the best performance, achieving 93.71 % accuracy and an AUC-ROC of 0.978. The obtainedresults indicate the potential of this architecture for integration into clinical decision-support systems

Maqola ma’lumotlari
MualliflarNazirova , Elmira, Abdusalomova , Shokhista
JurnalInnovation science and technologiy
Nashr sanasi2026-06-01
Jild2
Son6
TilIngliz
DOI10.5281/zenodo.20643398

Kalit so‘zlar

deep learning; skin disease diagnosis; convolutional neural network (CNN); ResNet; EfficientNet; dermoscopy; HAM10000; medical image analysis; transfer learning; AUC-ROC

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