AIMC Topic: Carcinoma, Basal Cell

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Deep learning-based non-invasive differential diagnosis of eyelid basal cell and sebaceous gland carcinomas using photographic images.

International ophthalmology
PURPOSE: Pathological examination, the current gold standard for differentiating eyelid basal cell carcinoma (BCC) and sebaceous gland carcinoma (SGC), is invasive, time-consuming, and often inaccessible in primary care hospitals. Therefore, a non-in...

Deep Learning Algorithms in the Diagnosis of Basal Cell Carcinoma Using Dermatoscopy: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: In recent years, deep learning algorithms based on dermatoscopy have shown great potential in diagnosing basal cell carcinoma (BCC). However, the diagnostic performance of deep learning algorithms remains controversial.

AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...

Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches.

Scientific reports
Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have shown significant global increases in burden, particularly among older adults, with wide regional, gender, and socio-demographic dispariti...

Detection of basal cell carcinoma by machine learning-assisted ex vivo confocal laser scanning microscopy.

International journal of dermatology
BACKGROUND: Ex vivo confocal laser scanning microscopy (EVCM) is an emerging imaging modality that enables near real-time histology of whole tissue samples. However, the adoption of EVCM into clinical routine is partly limited because the recognition...

Deep Learning for Automated Segmentation of Basal Cell Carcinoma on Mohs Micrographic Surgery Frozen Section Slides.

Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
BACKGROUND: Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localization of tumor on Mohs surgery (MMS) frozen section slides, have yet to reach clinical utility.

Transforming Skin Cancer Diagnosis: A Deep Learning Approach with the Ham10000 Dataset.

Cancer investigation
Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has the deadliest potential to spread to other parts of the body among all SCs. For SC treatments to be effective, early detection is essential. The high degree of similarit...

Weakly Supervised Classification of Mohs Surgical Sections Using Artificial Intelligence.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Basal cell carcinoma (BCC) is the most frequently diagnosed form of skin cancer, and its incidence continues to rise, particularly among older individuals. This trend puts a significant strain on health care systems, especially in terms of histopatho...

The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVE: The incidence of non-melanoma skin cancers, encompassing basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), is on the rise globally and new methods to improve skin malignancy diagnosis are necessary. This study aims t...