Ghosh, ArjanMandal, ShovonIslam, Md JahedulIslam, Kazi MinhazulDhar, SwarojaniBaidya, Rajesh2026-08-112026-08-112026-01-01A. Ghosh, S. Mandal, M. J. Islam, K. M. Islam, S. Dhar and R. Baidya, "Comparative Evaluation of Multiple CNN Architectures for Dermoscopic Skin Lesion Classification using ISIC Dataset," 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), Chittagong, Bangladesh, 2026, pp. 1-6, doi: 10.1109/QPAIN69676.2026.11545903.97983315499092-s2.0-105042742076https://hdl.handle.net/10361/28918Skin diseases pose a significant global health problem, and early diagnosis plays a vital role in improving patient outcomes, particularly for conditions such as Basal Cell Carcinoma, Dermatofibroma, Nevus, and Pigmented Benign Keratosis. This paper presents a systematic comparative evaluation of seven state-of-the-art Convolutional Neural Network architectures - InceptionV3, DenseNet-121, Xception, EfficientNetB3, ResNet152V2, MobileNetV2, and InceptionResNetV2 - for automated classification of dermoscopic images from a 9,857-image ISIC dataset. All models underwent identical preprocessing, data augmentation, and two-phase fine-tuning under controlled experimental conditions to eliminate pipeline bias. InceptionResNetV2 achieved superior performance with 93.5% test accuracy and 0.98 macro-AUC, demonstrating hybrid inception-residual architecture's effectiveness for multi-scale dermoscopic feature extraction. DenseNet-121 followed at 91.6% accuracy, while lightweight MobileNetV2 delivered 91.7% accuracy in 2.1 hours training time, suitable for mobile deployment. Basal Cell Carcinoma detection proved reliable across architectures (F 1=0.76-0.83), though class imbalance impacted Dermatofibroma performance. Confusion matrix analysis revealed Nevus-Pigmented Benign Keratosis overlap reflecting clinical similarity. Results establish clear deployment guidelines: InceptionResNetV2 for hospital diagnosis, MobileNetV2 for rural teledermatology, addressing dermatologist shortages in resource-limited settings like Bangladesh.6 pagesen-USfalseClass imbalanceCNN architecturesDenseNet-121Dermoscopic classificationFine-tuningInceptionResNetV2ISIC datasetMedical image analysisSkin lesion detectionTransfer learningComputational intelligence.Computer network architectures.Medical informatics.Conference Proceeding10.1109/QPAIN69676.2026.11545903