Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

Abstract

Dermatological conditions are a global health concern affecting all age groups. They encompass various skin issues such as rashes, moles, acne, blisters, hives, nodules, cysts, macules, and papules. Early diagnosis of dermatological conditions is crucial to prevent skin damage and chronic diseases. Recent advancements in artificial intelligence and medical image processing, particularly through deep learning, have significantly improved the precision and efficiency of dermatological disease detection by dermatologists. This paper thoroughly examines deep learning-based methods for detecting dermatological conditions from dermoscopic images. Specifically, it presents study of 22 methods that are used to detect dermatological conditions such as basal cell carcinoma, melanocytic nevus, seborrheic keratosis, psoriasis, benign keratosis, melanoma, acne, cold sore, warts, eczema, hives, shingles, scar, skin tag, inflammatory hyper pigment, cyst, dark circle, blackhead, burn and skin rash. It also covers clinical diagnostic methods for dermatological conditions and the need for computer-aided diagnosis. In this paper, we have also proposed the categorization of deep learning-based dermatological condition detection methods. Moreover, a comprehensive summary of these methods is presented. In addition, this paper summarizes the majority of the datasets available for computer-aided detection of dermatological conditions. Furthermore, it presents enormous challenges and potential future research directions. This survey informs researchers about the latest advancements in deep learning-based detection methods for dermatological conditions and allows dermatologists to stay updated on technological breakthroughs in this area.

Keywords

• Convolutional neural network
• classification
• dermatological condition
• deep learning
• dermoscopic image
• feature extraction