AI-Driven Dermatological Image Analysis for Early Melanoma Detection

Thesis Abstract

Early detection of melanoma remains a critical challenge in dermatology, owing to the difficulty in accurately distinguishing malignant from benign skin lesions at initial stages, often resulting in delayed treatment and increased mortality rates. The proliferation of dermatological imaging technologies combined with advancements in artificial intelligence (AI) offers promising avenues for enhancing diagnostic accuracy; however, there remains a significant gap in developing robust, automated systems capable of reliable melanoma detection across diverse populations. This study aims to develop and evaluate an AI-driven image analysis framework tailored for early melanoma diagnosis, with specific objectives to (1) compile a comprehensive dataset of dermoscopic images, (2) design and implement convolutional neural network (CNN) models optimized for skin lesion classification, (3) assess the models’ performance against dermatologists’ diagnoses, and (4) analyze the influence of demographic and lesion-specific variables on model accuracy. Employing a quantitative research design, the study gathered a dataset comprising 10,000 dermoscopic images sourced from multiple dermatology clinics and publicly accessible repositories, representing diverse ages, skin types, and lesion categories. A stratified sampling technique was employed to ensure balanced class distribution between melanoma and benign lesions. Data were annotated and validated by experienced dermatologists, serving as the ground truth for model training and testing. The core methodology involved developing and training CNN architectures—such as ResNet-50 and InceptionV3—using transfer learning techniques to leverage pre-trained weights, thereby enhancing model efficiency. Hyperparameter tuning was conducted via grid search, and data augmentation strategies addressed class imbalance and overfitting. Model evaluation entailed calculating performance metrics including accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Additionally, the study utilized Explainable AI (XAI) techniques, such as Grad-CAM visualizations, to interpret model decision processes. Anticipated findings suggest that the optimized CNN models will outperform traditional diagnostic methods, achieving accuracy rates exceeding 90%, sensitivity above 85%, and specificity near 88%. The models are expected to demonstrate significant potential for assisting dermatologists in early melanoma detection, especially in resource-constrained settings where specialist access is limited. Furthermore, the analysis is projected to reveal critical lesion characteristics and demographic factors influencing model performance, aligning with established dermatological knowledge and contributing to personalized diagnostic approaches. This research makes a substantive contribution to the field by integrating state-of-the-art deep learning techniques with dermatological image analysis, thus advancing automated diagnostic tools for melanoma. It extends existing literature by providing a comprehensive dataset, rigorous model evaluation, and interpretability of AI decisions, fostering trust and facilitating clinical integration. The study concludes that AI-driven image analysis systems can serve as effective adjuncts to human expertise, potentially reducing diagnostic disparities and improving patient outcomes. Recommendations include implementing the developed models into clinical workflows, expanding datasets to include multispectral imaging, and conducting longitudinal studies to assess real-world efficacy. Future research directions propose exploring multimodal data integration—combining clinical history, genetic markers, and imaging—to further enhance early detection accuracy and predictive capabilities in melanoma management.