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Line-field confocal optical coherence tomography of basal cell carcinoma: A retrospective study on diagnostic performance.
J Eur Acad Dermatol Venereol
December 2024
Department of Dermatology, Hôpital Erasme, HUB, Université Libre de Bruxelles, Brussels, Belgium.
Introduction: Line-field confocal optical coherence tomography (LC-OCT) represents one of the newest non-invasive in vivo skin imaging techniques. Previous studies described morphologic criteria of basal cell carcinoma (BCC) under LC-OCT examination. Preliminary data on LC-OCT diagnostic performance for BCC have recently been published but showed only a modest improvement compared to dermoscopy, possibly due to study limitations.
View Article and Find Full Text PDFEnhanced MobileNet for skin cancer image classification with fused spatial channel attention mechanism.
Sci Rep
November 2024
Department of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.
Skin Cancer, which leads to a large number of deaths annually, has been extensively considered as the most lethal tumor around the world. Accurate detection of skin cancer in its early stage can significantly raise the survival rate of patients and reduce the burden on public health. Currently, the diagnosis of skin cancer relies heavily on human visual system for screening and dermoscopy.
View Article and Find Full Text PDFMore than one shade of pink as a marker of early amelanotic/hypomelanotic melanoma.
J Dermatol
July 2024
Cancer Epidemiology Unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy.
Article Synopsis
- * The study analyzed 133 digital images of skin lesions diagnosed as either early amelanotic melanoma or non-melanocytic lesions, using assessments by three blinded dermatologists.
- * Results indicated specific features in lesions (like multiple shades of pink and irregular dots) can significantly increase the likelihood of accurate diagnosis of early amelanotic melanoma compared to non-melanoma lesions.
Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging.
Biomed Opt Express
August 2024
University of Oklahoma, School of Electrical and Computer Engineering, Norman, OK, USA.
Article Synopsis
- There is currently no objective clinical tool for primary care physicians or dermatologists to identify potentially cancerous skin lesions.
- The study explored the use of multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy to differentiate between malignant and benign pigmented skin lesions through advanced imaging techniques and machine learning models.
- The deep neural network model outperformed others, achieving a sensitivity of 76.84% and specificity of 78.29%, indicating that maFLIM dermoscopy could help doctors in early detection and reduce unnecessary biopsies.
Melanoma Breslow Thickness Classification using Ensemble-based Knowledge Distillation with Semi-supervised Convolutional Neural Networks.
IEEE J Biomed Health Inform
September 2024
Melanoma is considered a global public health challenge and is responsible for more than 90% deaths related to skin cancer. Although the diagnosis of early melanoma is the main goal of dermoscopy, the discrimination between dermoscopic images of in situ and invasive melanomas can be a difficult task even for experienced dermatologists. Recent advances in artificial intelligence in the field of medical image analysis show that its application to dermoscopy with the aim of supporting and providing a second opinion to the medical expert could be of great interest.
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