Melanoma is one of the deadliest forms of skin cancer due to its rapid progression and high metastatic potential. Early detection greatly improves survival rates, yet traditional methods relying on dermatologist expertise are subjective and prone to error. This systematic review analyzes recent advances (2022–2024) in deep learning (DL) for melanoma detection. Key approaches include convolutional neural networks (CNNs), transfer learning, ensemble systems, attention mechanisms, and lightweight models suitable for mobile deployment. Performance metrics across public datasets such as ISIC, HAM10000, and PH2 indicate state-of-the-art results with improved sensitivity and specificity. Despite progress, challenges such as dataset bias, limited diversity, interpretability, and clinical validation remain. Future directions include explainable AI, federated learning, and integration of multimodal data. The findings demonstrate DL’s transformative potential for dermatology, but emphasize the need for real-world validation before clinical adoption.

The integration of deep learning (DL) methodologies into the domain of melanoma detection has revolutionized traditional diagnostic workflows, offering unprecedented accuracy and efficiency in the early identification of malignant lesions. Over the past few years, significant progress has been made, transitioning from basic convolutional neural networks (CNNs) to sophisticated ensemble models, attention-based frameworks, and lightweight architectures optimized for mobile deployment. This evolution has not only enhanced the predictive performance of diagnostic systems but has also expanded the possibilities for widespread, accessible, and real-time melanoma screening, thereby potentially improving patient outcomes through earlier intervention. One of the most transformative impacts of deep learning in melanoma detection is the achievement of diagnostic performance that rivals or even exceeds that of experienced dermatologists in controlled experimental settings. Models such as ResNet, Inception-v3, EfficientNet, and MobileNet, when fine-tuned on dermoscopic datasets, have demonstrated high sensitivity and specificity, often surpassing human diagnostic benchmarks. The success of these models can be attributed to their ability to learn complex, hierarchical features from dermoscopic images that may not be immediately perceptible to the human eye. The adoption of transfer learning and pre-training strategies has further accelerated model development, enabling high accuracy even with relatively limited dermatological data. Despite these accomplishments, the field has not remained static. Innovation in model design continues to play a crucial role in advancing melanoma detection capabilities. Ensemble learning strategies, which combine the outputs of multiple deep learning models, have been shown to enhance classification robustness, reduce variance, and improve generalization across diverse datasets. Such ensembles harness the complementary strengths of different architectures, ensuring that the weaknesses of one model are compensated for by the strengths of others.

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