Deep Learning Based Classification of Dermatological Disorders

Abstract

Automated medical diagnosis has become crucial and significantly supports medical doctors. Thus, there is a demand for inventing deep learning (DL) and convolutional networks for analyzing medical images. Dermatology, in particular, is one of the domains that was recently targeted by AI specialists to introduce new DL algorithms or enhance convolutional neural network (CNN) architectures. A significantly high proportion of studies in the field are concerned with skin cancer, whereas other dermatological disorders are still limited. In this work, we examined the performance of 6 CNN architectures named VGG16, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet50 for the top 3 dermatological disorders that frequently appear in the Middle East. An Image filtering and denoising were imposed in this work to enhance image quality and increase architecture performance. Experimental results revealed that MobileNet achieved the highest performance and accuracy among the CNN architectures and can classify disorder with high performance (95.7% accuracy). Future scope will focus more on proposing a new methodology for deep-based classification. In addition, we will expand the dataset for more images that consider new disorders and variations.

Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence

“…Goceri [23] used MobileNet V2 in a mobile phone application with a user-friendly interface and demonstrated that the proposed method accurately diagnosed dermatological diseases with 94.76% accuracy. AlSuwaidan [53] evaluated the performance of six CNNs (VGG16, EfficientNet, InceptionV3, MobileNet, NasNet and ResNet50) for the three most common dermatological conditions (eczema, atopic dermatitis, and psoriasis) in the Middle East, and MobileNet had the highest accuracy (95.7%). Li et al [54] also showed the performance of different CNNs for the diagnosis of actinic keratosis with an accuracy of about 92%.…”

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

“…Deep learning algorithms, trained on large datasets of medical images, have demonstrated remarkable accuracy in diagnosing various conditions, from skin cancers to retinal diseases 11 . Which means in dermatology, AI has the potential to analyze complex patterns in skin and scalp images, offering objective and consistent diagnoses 12 . Especially deep neural transfer learning, involves applying knowledge gained while solving one problem to a different but related problem.…”

“… 11 Which means in dermatology, AI has the potential to analyze complex patterns in skin and scalp images, offering objective and consistent diagnoses. 12 Especially deep neural transfer learning, involves applying knowledge gained while solving one problem to a different but related problem. For dermatology, this means using neural networks pre‐trained on vast datasets (not necessarily medical) and fine‐tuning them for specific tasks like diagnosing hair and scalp disorders.…”

“…11 Which means in dermatology, AI has the potential to analyze complex patterns in skin and scalp images, offering objective and consistent diagnoses. 12 Especially deep neural transfer learning, involves applying knowledge gained while solving one problem to a different but related problem.…”

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection

“…Dermatologists often rely on their clinical expertise to diagnose these diseases, which can be time-consuming and subject to human error. Machine learning techniques, such as deep learning, have shown great promise in aiding the early detection and diagnosis of skin diseases like eczema and psoriasis [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”