AIMC Topic: Skin Diseases

Clear Filters Showing 31 to 40 of 202 articles

The Comparative Sufficiency of ChatGPT, Google Bard, and Bing AI in Answering Diagnosis, Treatment, and Prognosis Questions About Common Dermatological Diagnoses.

JMIR dermatology
Our team explored the utility of unpaid versions of 3 artificial intelligence chatbots in offering patient-facing responses to questions about 5 common dermatological diagnoses, and highlighted the strengths and limitations of different artificial in...

Dynamic Subcluster-Aware Network for Few-Shot Skin Disease Classification.

IEEE transactions on neural networks and learning systems
This article addresses the problem of few-shot skin disease classification by introducing a novel approach called the subcluster-aware network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SC...

Attention-Guided Learning With Feature Reconstruction for Skin Lesion Diagnosis Using Clinical and Ultrasound Images.

IEEE transactions on medical imaging
Skin lesion is one of the most common diseases, and most categories are highly similar in morphology and appearance. Deep learning models effectively reduce the variability between classes and within classes, and improve diagnostic accuracy. However,...

Artificial Intelligence-Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study.

Journal of medical Internet research
BACKGROUND: Private-part skin diseases (PPSDs) can cause a patient's stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing t...

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

JMIR dermatology
BACKGROUND: Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be m...

AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation.

PloS one
Skin lesion segmentation plays a pivotal role in the diagnosis and treatment of skin diseases. By using deep neural networks to segment lesion areas, doctors can more accurately assess the severity of health-related conditions of patients and promptl...

AI in Aesthetic/Cosmetic Dermatology: Current and Future.

Journal of cosmetic dermatology
BACKGROUND: Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of st...

A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.

Journal of imaging informatics in medicine
Skin diseases are a significant global public health concern, affecting 21-85% of the world's population, particularly those in low- and middle-income countries. Accurate and timely diagnosis is crucial for effective treatment and improved patient ou...

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

International journal of computer assisted radiology and surgery
PURPOSE: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed s...

MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.

Journal of imaging informatics in medicine
The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel skin disease classification model leveraging advanced deep learning techniques. The proposed architecture combines the M...