Machine learning and its application in skin cancer
Artificial intelligence (AI) has wide applications in healthcare, including dermatology.
Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can …
Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can …
[HTML][HTML] Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities
Recently, there has been great interest in develo** Artificial Intelligence (AI) enabled
computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing …
computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing …
A deep learning system for differential diagnosis of skin diseases
Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most
cases are seen instead by general practitioners with lower diagnostic accuracy. We present …
cases are seen instead by general practitioners with lower diagnostic accuracy. We present …
Single model deep learning on imbalanced small datasets for skin lesion classification
Deep convolutional neural network (DCNN) models have been widely explored for skin
disease diagnosis and some of them have achieved the diagnostic outcomes comparable or …
disease diagnosis and some of them have achieved the diagnostic outcomes comparable or …
Melanoma detection using deep learning-based classifications
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more
common as the population ages. As a general rule, the earlier skin cancer can be …
common as the population ages. As a general rule, the earlier skin cancer can be …
Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach
Melanoma is considered as one of the fatal cancer in the world, this form of skin cancer may
spread to other parts of the body in case that it has not been diagnosed in an early stage …
spread to other parts of the body in case that it has not been diagnosed in an early stage …
Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification
Deep learning has been used to analyze and diagnose various skin diseases through
medical imaging. However, recent researches show that a well-trained deep learning model …
medical imaging. However, recent researches show that a well-trained deep learning model …
Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting
Objective: This paper addresses two key problems of skin lesion classification. The first
problem is the effective use of high-resolution images with pretrained standard architectures …
problem is the effective use of high-resolution images with pretrained standard architectures …
Dermgan: Synthetic generation of clinical skin images with pathology
Despite the recent success in applying supervised deep learning to medical imaging tasks,
the problem of obtaining large and diverse expert-annotated datasets required for the …
the problem of obtaining large and diverse expert-annotated datasets required for the …
CI-Net: Clinical-inspired network for automated skin lesion recognition
The lesion recognition of dermoscopy images is significant for automated skin cancer
diagnosis. Most of the existing methods ignore the medical perspective, which is crucial …
diagnosis. Most of the existing methods ignore the medical perspective, which is crucial …