AIMC Topic: Dermoscopy

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Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model.

IEEE transactions on medical imaging Dec 1, 2016
We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, f...
Melanoma Skin Neoplasms Dermoscopy Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Unsupervised delineation of stratum corneum using reflectance confocal microscopy and spectral clustering.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) Aug 12, 2016
BACKGROUND: Measuring the thickness of the stratum corneum (SC) in vivo is often required in pharmacological, dermatological, and cosmetological studies. Reflectance confocal microscopy (RCM) offers a non-invasive imaging-based approach. However, RCM...
Microscopy, Interference Pattern Recognition, Automated Skin Aging Humans Epidermal Cells Dermoscopy Reproducibility of Results Sensitivity and Specificity Microscopy, Confocal Unsupervised Machine Learning Image Interpretation, Computer-Assisted Observer Variation
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Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.

BioMed research international Jun 13, 2016
Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less exte...
Image Enhancement Software Design Nevus Sensitivity and Specificity Diagnosis, Computer-Assisted Support Vector Machine Early Diagnosis Melanoma Skin Neoplasms Dermoscopy Reproducibility of Results Humans Algorithms
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Adaptable texture-based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) Mar 16, 2016
BACKGROUND: Pink blush is a common feature in basal cell carcinoma (BCC). A related feature, semitranslucency, appears as smooth pink or orange regions resembling skin color. We introduce an automatic method for detection of these features based on s...
Machine Learning Algorithms Colorimetry Female Humans Carcinoma, Basal Cell Color Skin Neoplasms Dermoscopy Image Interpretation, Computer-Assisted Sensitivity and Specificity Reproducibility of Results Pattern Recognition, Automated Feedback Aged Male Middle Aged
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Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) Mar 22, 2015
BACKGROUND/PURPOSE: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature ...
Colorimetry Color Humans Dermoscopy Supervised Machine Learning Algorithms Image Interpretation, Computer-Assisted Reproducibility of Results Melanoma Skin Neoplasms Pattern Recognition, Automated Skin Pigmentation Sensitivity and Specificity Computer Systems
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Four-class classification of skin lesions with task decomposition strategy.

IEEE transactions on bio-medical engineering Aug 15, 2014
This paper proposes a new computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an ai...
Colorimetry Image Interpretation, Computer-Assisted Skin Neoplasms Humans Pattern Recognition, Automated Dermoscopy Artificial Intelligence Image Enhancement Reproducibility of Results Algorithms Sensitivity and Specificity
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Early diagnosis model of mycosis fungoides and five inflammatory skin diseases based on a multimodal data-based convolutional neural network.

The British journal of dermatology Oct 17, 2025
BACKGROUND: Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphoma, and early-stage MF is difficult to differentiate from erythematous inflammatory disease. With the exception of biopsy, noninvasive information such as a patient'...
Aged Adult Male Middle Aged Retrospective Studies Humans Female Neural Networks, Computer Skin Neoplasms Dermoscopy Dermatologists Deep Learning Early Diagnosis Convolutional Neural Networks Mycosis Fungoides Diagnosis, Differential Dermatitis Skin
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Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jul 1, 2025
Melanonychia, a condition that can be indicative of malignant melanoma, presents a significant challenge in early diagnosis due to the invasive nature and equipment dependency of traditional diagnostic methods such as nail biopsy and dermatoscope ima...
Cross-Sectional Studies Melanoma Deep Learning Dermoscopy Smartphone Reproducibility of Results Image Interpretation, Computer-Assisted Sensitivity and Specificity Humans Nails Nail Diseases
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Self-supervised multi-modality learning for multi-label skin lesion classification.

Computer methods and programs in biomedicine Jun 1, 2025
BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide detailed views of surface structures, while clinical images offer complementary macroscopic information. Clini...
Cluster Analysis Supervised Machine Learning Image Interpretation, Computer-Assisted Skin Neoplasms Dermoscopy Diagnosis, Computer-Assisted Deep Learning Humans Neural Networks, Computer Algorithms Melanoma
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Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study.

Nature communications May 21, 2025
Artificial intelligence (AI) systems substantially improve dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing their confidence and trust in AI-driven decisions. Despite these advancements, there rema...
Humans Dermoscopy Adult Male Middle Aged Artificial Intelligence Female Melanoma Skin Neoplasms Eye-Tracking Technology Dermatologists Nevus
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