With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …

Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin
lesions at an early stage could aid clinical decision-making by providing an accurate …

In clinical applications, neural networks must focus on and highlight the most important parts
of an input image. Soft-Attention mechanism enables a neural network to achieve this goal …

The skin lesion types result in delayed diagnosis due to high similarity in early stages of the
skin cancer. In this regard, deep learning algorithms are well-recognized solutions; however …

Abstract Generally, Melanoma, Merkel cell cancer, Squamous cell carcinoma, and Basal cell
carcinoma, are the four major categories of skin cancers. In contrast to other cancer types …

Recently, multimodal image-based methods have shown great performance in skin cancer
diagnosis. These methods usually use convolutional neural networks (CNNs) to extract the …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …