Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I.; Ali, H.; Karaouni, M. Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning. BioMedInformatics2024, 4, 638-660.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I.; Ali, H.; Karaouni, M. Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning. BioMedInformatics 2024, 4, 638-660.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I.; Ali, H.; Karaouni, M. Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning. BioMedInformatics2024, 4, 638-660.
Abou Ali, M.; Dornaika, F.; Arganda-Carreras, I.; Ali, H.; Karaouni, M. Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning. BioMedInformatics 2024, 4, 638-660.
Abstract
Amidst the burgeoning global concerns surrounding skin cancer, the quest for precise and efficient diagnostic methodologies has intensified. Conventional diagnostic approaches relying on subjective visual inspection by dermatologists often grapple with subjectivity and resource constraints. This predicament underscores the critical role of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms by significantly enhancing accuracy and accessibility. This study embarks on an extensive exploration of skin cancer classification, specifically targeting the intricate task of 8-class skin cancer classification. Employing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis was conducted utilizing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering "Naturalize" augmentation technique was introduced, leading to the creation of indispensable tools—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer becomes evident through this research. AI’s proficiency in analyzing vast datasets and discerning subtle patterns augments the diagnostic prowess of dermatologists significantly. Integration of AI-powered systems in skin cancer diagnosis holds immense promise in facilitating early detection and substantially improving patient outcomes. The meticulous evaluation employed quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations. Astonishingly, the culmination of these endeavors culminated in an unprecedented achievement—100% average accuracy, precision, recall, and F1-Score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. This remarkable feat underscores the transformative potential of AI-driven methodologies, exemplifying their prowess in redefining the landscape of skin cancer diagnosis and management. In summary, this research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care.
Keywords
Convolutional Neural Net (CNN); Vision Transformer (ViT); ImageNet Models; Transfer Learning (TL); Machine Learning (ML); Deep Learning (DP); Skin Cancer; Naturalize
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
