AI Medical Compendium Topic

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Skin

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Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

PloS one
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposiu...

Machine learning for the prediction of sunscreen sun protection factor and protection grade of UVA.

Experimental dermatology
We report a prediction model for sunscreen sun protection factor (SPF) and protection grade of ultraviolet (UV) A (PA) based on machine learning. We illustrate with real clinical test results of UV protection ability of sunscreen for SPF and PA. With...

Data augmentation in dermatology image recognition using machine learning.

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)
BACKGROUND: Each year in the United States, over 80 million people are affected by acne, atopic dermatitis, rosacea, psoriasis, and impetigo. Artificial intelligence and machine learning could prove to be a good tool for assisting in the diagnosis of...

Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like re...

Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imb...

A comparative study of deep learning architectures on melanoma detection.

Tissue & cell
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instrumen...

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Forensic science international. Genetics
Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composit...

Bioinspired Hairy Skin Electronics for Detecting the Direction and Incident Angle of Airflow.

ACS applied materials & interfaces
The human skin has inspired multimodal detection using smart devices or systems in fields including biomedical engineering, robotics, and artificial intelligence. Hairs of a high aspect ratio (AR) connected to follicles, in particular, detect subtle ...