Deep learning has played a major role in the interpretation of dermoscopic images for detecting skin defects and abnormalities. However, current deep solutions dermatological lesion analysis are typically limited providing probabilistic predictions which highlights importance concerning uncertainties. This concept uncertainty can provide confidence level each feature prevents overconfident with...

Skin cancers are increasing at an alarming rate, and detection in the early stages is essential for advanced treatment. The current segmentation methods have limited labeling ability to ground truth images due numerous noisy expert annotations present datasets. precise boundary correctly locate diagnose various skin lesions. In this work, lesion method proposed as a Markov decision process. It ...

Background Convolutional neural networks (CNNs) are a type of artificial intelligence that shows promise as diagnostic aid for skin cancer. However, the majority trained using retrospective image data sets with varying capture standardization. Objective The aim our study was to use CNN models same architecture—trained on acquired either device and technique (standardized) or varied devices tech...