… a deep learning architecture (ResNet-152) to classify the clinical images of 12 skin diseases
… dermoscopic datasets of skin lesions, deep learning algorithms do not work the same way …

… for the development of machine learning (ML) algorithms with … Lastly, we explore potential
challenges and limitations for the … cutaneous diseases: psoriasis, psoriatic arthritis, and skin …

… , as well as opportunities that we … of deep learning methods in skin disease diagnosis tasks
in section 6. Then we highlight the challenges in the area of skin disease diagnosis with deep …

… to prescribe medication based on patients’ symptoms [3]. This work presents a robust …
Deep learning models can solve critical problems by automatically identifying the input data …

… , challenges and opportunities for melanoma diagnosis and investigate the existing solutions
for the diagnosis of melanoma detection using deep learning… , challenges and opportunities …

… effective and efficient learning despite having very little training data to work with. When …
tested in both tasks, proving that machine learning is capable of recognizing skin cancer at a …

… to experts on binary classification tasks (benign versus malignant) or on skin lesion … 419
skin conditions seen in this work. Our DLS provides several advances relative to previous work. …

… work identified the main challenges of evaluating skin lesion segmentation and classification
… They used three CNN models with transfer learning to classify five skin diseases of the face…

… From the type of CNN used, the research work collected in this study adopts popular CNN
… Based on these constraints, we observed that deep learning performance is better than that …

… skin diseases is very challenging due to the symptom of skin … Especially, those features are
directly used, when network train on … interests include deep learning, machine learning and …