ADDRESSING IMBALANCE ISIC-2019 DATASET IN DERMOSCOPIC PIGMENTED SKIN LESION CLASSIFICATION

Deep Learning-Based Automated Skin Cancer Detection Using Dermoscopic Images

The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.

A Comparative Evaluation of Pre-trained Convolutional Neural Networks for Melanoma Detection

Early diagnosis of melanoma is critical for improving patient survival rates. However, accurately distinguishing melanoma from other skin lesions remains a significant clinical challenge due to the high visual similarity among lesion types and variability in image acquisition conditions. Artificial intelligence, partic...

AI-Based Skin Disease Classification Using Deep Learning Techniques

This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images, built on the ResNet50 convolutional neural network architecture and evaluated using standard classification metrics.

An Explainable Deep Learning Framework for Skin Cancer Detection Using Dermoscopic Images

The results show that both the CNN architectures used for the experiment and the automated classification of skin lesions using them are effective, and the MobileNetV2 performs the best in the experimental framework of this study.

Enhanced ConvNeXt model for dermoscopic skin lesion classification with cross-domain generalization

A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer

Background/Objectives: Cutaneous melanoma caused over 331,000 new cases and 58,000 deaths worldwide in 2022, with five-year survival falling from 99.4% in localised disease to 35.6% after distant metastasis. Most dermoscopic deep learning studies report headline accuracy without addressing data leakage, calibration, or...