Diagnostika melanoma z novimi "deep-learning" metodami - Joveski, Luka (Placebo, 2025)
Melanoma is one of the most dangerous forms of skin cancer, with early detection significantly improving survival rates. Traditional diagnostic methods, such as clinical examination and dermoscopy, are reliable but subjective and dependent on the experience of the physician. Advances in artificial intelligence, particularly deep learning, offer new possibilities for automated, accurate, and faster melanoma diagnosis. Deep neural network models, trained on extensive image datasets, have already achieved comparable or even better accuracy than dermatologists. However, several challenges remain, such as imbalanced data, lack of metadata, ethical dilemmas, and difficulty transferring models to clinical practice. The integration of genetic and clinical data of the patients, along with the development of lightweight and robust architectures, can contribute to more personalised and widely accessible patient care through advanced deep learning methods. The aim of this review is to present general deep learning approaches in dermoscopy of cutaneous melanoma, to evaluate their effectiveness, and to highlight the key advantages, challenges, and opportunities for clinical application.
Keywords: convolutional neural networks (CNN); cutaneous melanoma; deep learning; dermatoskopija; dermoscopy; diagnosis; Diagnostika; globoko učenje; konvolucijske nevronske mreže (CNN); kožni melanom; Melanom