AIMC Topic: Nevus

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Skin lesion classification with ensembles of deep convolutional neural networks.

Journal of biomedical informatics Aug 10, 2018
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic mel...
Keratosis, Seborrheic Melanocytes Dermoscopy Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Nevus ROC Curve Melanoma Skin Neoplasms Algorithms Databases, Factual Neural Networks, Computer Machine Learning Humans
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Dermatologist-level classification of skin cancer with deep neural networks.

Nature Jan 25, 2017
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of ski...
Photography Cell Phone Keratosis, Seborrheic Nevus Melanoma Humans Datasets as Topic Skin Neoplasms Neural Networks, Computer Automation Reproducibility of Results Dermatologists Keratinocytes
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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...
Software Design Nevus Dermoscopy Diagnosis, Computer-Assisted Melanoma Early Diagnosis Support Vector Machine Reproducibility of Results Sensitivity and Specificity Skin Neoplasms Humans Image Enhancement Algorithms
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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...
Skin Neoplasms Dermoscopy Adult Female Melanoma Eye-Tracking Technology Dermatologists Nevus Male Middle Aged Humans Artificial Intelligence
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Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is pa...
Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Nevus Fundus Oculi Neural Networks, Computer Choroid Neoplasms Algorithms
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Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks.

Journal of X-ray science and technology Jan 1, 2024
BACKGROUND: With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in th...
Humans Neural Networks, Computer Deep Learning Nevus Skin Neoplasms Diagnosis, Computer-Assisted
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Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2018
BACKGROUND: Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment.
Diagnosis, Differential Nevus Humans Dermoscopy Image Processing, Computer-Assisted Neural Networks, Computer Skin Neoplasms
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