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

Automated skin lesion classification in dermoscopy images is an essential way to improve
the diagnostic performance and reduce melanoma deaths. Although deep convolutional …

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of
skin cancers have been rising over the past decades. In recent years, use of dermoscopy …

Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

We propose a multitask deep convolutional neural network, trained on multimodal data
(clinical and dermoscopic images, and patient metadata), to classify the 7-point melanoma …

Automated skin lesion segmentation and classification are two most essential and related
tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep …

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have
recently been proposed for automatic glaucoma detection based on fundus images …

Importance Convolutional neural networks (CNNs) achieve expert-level accuracy in the
diagnosis of pigmented melanocytic lesions. However, the most common types of skin …

Recently, the attention mechanism has been successfully applied in convolutional neural
networks (CNNs), significantly boosting the performance of many computer vision tasks …

While convolutional neural networks (CNNs) have successfully been applied for skin lesion
classification, previous studies have generally considered only a single clinical/macroscopic …