Automated Skin Disease Detection Using an Optimized Xception-Based Framework with Comparative Deep Learning Analysis

Registration ID: IJNRD_327668

Published ID: IJNRD2608076

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V. Akhila, Dr. B. Sateesh Kumar

Skin Disease Detection, Convolutional Neural Networks, Transfer Learning, Deep Learning, Explainable Artificial Intelligence, Dermoscopic Image Classification.”

Skin diseases and hematological disorders are among the most prevalent health conditions worldwide, where timely and accurate diagnosis is essential for effective treatment and improved clinical outcomes. Conventional diagnosis relies heavily on manual visual assessment by medical experts, making the process time-consuming, subjective, and susceptible to inconsistent predictions, particularly when diseases exhibit similar visual characteristics. Four publicly available medical image datasets, namely HAM10000, ISIC 2019–2020 Melanoma, Skin Cancer (Benign vs. Malignant), and Blood Cell Images, were utilized for comparative training and evaluation. The framework incorporates comprehensive preprocessing, including hair removal, image resizing, data augmentation, and image normalization, to enhance image quality and improve feature representation. Multiple deep learning architectures, including EfficientNetV2, InceptionResNetV2, InceptionV3, MobileNet, VGG19, ResNet50, Xception, ConvNeXt-Tiny, and a hybrid EDA-ResNet50 integrating Convolutional Block Attention Module and Coordinate Attention, were comparatively evaluated. Performance was assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrated that Xception achieved the highest classification accuracy of 98.70% on the Blood Cell dataset, 82.30% on HAM10000, and 89.70% on the Skin Cancer dataset, while InceptionResNetV2, InceptionV3, and MobileNetV1 each achieved 94.30% on the ISIC dataset. The framework further integrates Grad-CAM-based explainability and prediction confidence estimation, providing an accurate, interpretable, and reliable computer-aided medical image classification solution for automated healthcare support.

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V. Akhila & Dr. B. Sateesh Kumar (August-2026). Automated Skin Disease Detection Using an Optimized Xception-Based Framework with Comparative Deep Learning Analysis. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(8), a672-a680. https://ijnrd.org/papers/IJNRD2608076.pdf

Volume 11 Issue 8, August-2026

Pages : a672-a680

Paper Reg. ID: IJNRD_327668

Published Paper Id: IJNRD2608076

Research Area: Other area not in list

Author Type: Indian Author

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2608076.pdf

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