Artificial Intelligence for Sustainable Dermatology in Smart Green Cities: Exploring Deep Learning Models for Accurate Skin Lesion Recognition

Abstract

Smart and sustainable dermatology takes on a new dimension within Green Smart Cities with the integration of artificial intelligence (AI) into dermatological diagnosis. This study explores the success of deep learning models in accurately recognizing skin lesions, focusing on the use of the HAM10000 dataset. Our comparative analysis highlights the crucial impact of network architecture choices, data augmentation, and preprocessing on model performance. The results reveal that models leveraging transfer learning and fine-tuning on pre-trained networks excel in precision, underscoring their relevance in the context of smart green health. We also address opportunities for improvement in model generalization across diverse datasets and skin types. These findings provide a foundation for the development of more accurate skin lesion recognition models aligned with the principles of Green Smart Health, contributing to faster diagnostics, improved patient care, and ultimately, healthier Green Smart Cities. This work opens avenues for future research, such as exploring of the effectiveness of deep learning techniques in diverse health contexts and the integration of clinical data for more personalized dermatological diagnostics within Green Smart Cities.