AIMC Topic: Dermoscopy

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Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

Computers in biology and medicine Oct 12, 2024
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a nov...
Color Skin Image Interpretation, Computer-Assisted Melanoma Skin Neoplasms Dermoscopy Humans Neural Networks, Computer Algorithms Databases, Factual
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MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.

Journal of imaging informatics in medicine Oct 1, 2024
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...
Deep Learning Skin Diseases Image Interpretation, Computer-Assisted Humans Databases, Factual Dermoscopy
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Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

Scientific reports Aug 26, 2024
This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the Internationa...
Humans Skin Neoplasms Skin Deep Learning Dermoscopy Skin Diseases Image Interpretation, Computer-Assisted ROC Curve Radiomics Image Processing, Computer-Assisted
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Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

European journal of cancer (Oxford, England : 1990) Aug 25, 2024
IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic cate...
Melanocytes Female Humans Diagnosis, Differential Melanoma Dermoscopy Neural Networks, Computer Dermatologists Skin Neoplasms Cross-Sectional Studies Algorithms Image Interpretation, Computer-Assisted ROC Curve
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MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Sensors (Basel, Switzerland) Aug 20, 2024
Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remai...
Deep Learning Image Processing, Computer-Assisted Skin Image Interpretation, Computer-Assisted Neural Networks, Computer Algorithms Dermoscopy Humans
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Semantic segmentation in skin surface microscopic images with artifacts removal.

Computers in biology and medicine Aug 17, 2024
Skin surface imaging has been used to examine skin lesions with a microscope for over a century and is commonly known as epiluminescence microscopy, dermatoscopy, or dermoscopy. Skin surface microscopy has been recommended to reduce the necessity of ...
Skin Microscopy Artifacts Deep Learning Image Processing, Computer-Assisted Humans Algorithms Dermoscopy
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EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.

Journal of imaging informatics in medicine Aug 15, 2024
Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations i...
Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms Melanoma Skin Neoplasms Dermoscopy
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Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

International journal of computer assisted radiology and surgery Aug 2, 2024
PURPOSE: The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy ...
Humans Neural Networks, Computer Melanoma Image Interpretation, Computer-Assisted Datasets as Topic Deep Learning Image Processing, Computer-Assisted Skin Neoplasms Data Mining Dermoscopy
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Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.

BMC medical imaging Aug 2, 2024
Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated...
Humans Neural Networks, Computer Skin Neoplasms Dermoscopy Deep Learning Image Interpretation, Computer-Assisted
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Deep learning-assisted multispectral imaging for early screening of skin diseases.

Photodiagnosis and photodynamic therapy Jul 26, 2024
INTRODUCTION: Melanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This proces...
Skin Diseases Warts Dermoscopy Humans Early Diagnosis Deep Learning Nevus, Pigmented Skin Neoplasms Psoriasis Keratosis, Seborrheic
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