Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology

Abstract

This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.

Progress and challenges of artificial intelligence in skin of color

“…This technology is a valuable adjunct to dermatologists, enhancing diagnostic accuracy and clinical decisionmaking, which can significantly improve patient outcomes. 1 Despite remarkable progress, challenges still persist, particularly in applying AI to individuals with Skin of Color (SOC). A recent review by Fliorent et al 2 stated that the challenges associated with applying AI to SOC in the context of dermatology arise from several factors, including the constrained scope of the Fitzpatrick skin type (FST), insufficient representation of SOC in datasets, and concerns related to image quality and standardization.…”