AIMC Topic: Dermoscopy

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Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports
Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer ...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Scientific reports
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...

Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.

Scientific reports
Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To addr...

Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.

Scientific reports
In the human body, the skin is the main organ. Nearly 30-70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermosco...

A multimodal skin lesion classification through cross-attention fusion and collaborative edge computing.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Skin cancer is a significant global health concern requiring early and accurate diagnosis to improve patient outcomes. While deep learning-based computer-aided diagnosis (CAD) systems have emerged as effective diagnostic support tools, they often fac...

Diagnosis melanoma with artificial intelligence systems: A meta-analysis study and systematic review.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: One of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods.

Updated Techniques for Melanoma Diagnosis.

Dermatologic clinics
Melanoma, an aggressive skin cancer, requires timely diagnostics for improved patient outcomes. The ABCDE criteria-assessing asymmetry, borders, color, diameter, and evolution-serve as foundational guidelines for early detection. Non-invasive tools l...

Enhancing Diagnosis of Psoriasis and Inflammatory Skin Diseases: A Spatially Aligned Multimodal Model Integrating Clinical and Dermoscopic Images.

The Journal of investigative dermatology
Psoriasis is a chronic inflammatory disease with significant physical and psychological impacts. To overcome the limitations of single-modality artificial intelligence models in diagnosing inflammatory skin diseases, we propose a multimodal framework...

A novel hybrid feature fusion approach using handcrafted features with transfer learning model for enhanced skin cancer classification.

Computers in biology and medicine
Skin cancer is a deadly disease and has the highest rising rates globally. It arises from aberrant skin cells, which are often caused by prolonged exposure to ultraviolet rays from sunlight or artificial tanning devices. Dermatologists rely on visual...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Scientific reports
Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the ea...