Latest AI and machine learning research in dermatology for healthcare professionals.
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using A...
BACKGROUND: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to identify methods that ensure consistent diagnostic accuracy and maintain image quality. Deep progressive learning...
Purpose To evaluate two automated tools for detecting lesions on fluorine 18 (F) fluoroestradiol (FES) PET/CT images and assess concordance of F-FES P...
Diabetic retinopathy (DR), a serious ocular complication of diabetes, is one of the primary causes of vision loss among retinal vascular diseases. D...
Photon Absorption Remote Sensing (PARS) enables label-free imaging of subcellular morphology by observing biomolecule specific absorption interactio...
The recent global spread of monkeypox, particularly in regions where it has not historically been prevalent, has raised significant public health co...
To improve the segmentation of diabetic retinopathy lesions (microaneurysms, hemorrhages, exudates, and soft exudates), we implemented a binary segm...
Cattle lameness is often caused by hoof injuries or interdigital dermatitis, leads to pain and significantly impacts essential physiological activit...
Due to physiological changes during the perimenopausal period, the morphology of cervical cells undergoes certain alterations. Accurate cell image seg...
This study performs a comprehensive evaluation of quantitative measurements as extracted from automated deep-learning-based segmentation methods, be...
Automated diagnosis of eczema using images acquired from digital camera can enable individuals to self-monitor their recovery. The process entails f...
Deep learning has been reported to achieve high performances in the detection of skin cancer, yet many challenges regarding the reproducibility of r...
Medical image segmentation remains challenging due to the high cost of pixel-level annotations for training. In the context of weak supervision, cli...
Accurate segmentation of longitudinal CT scans is important for monitoring tumor progression and evaluating treatment responses. However, existing 3...
The joint interpretation of multi-modal and multi-view fundus images is critical for retinopathy prevention, as different views can show the complet...
According to the National Rosacea Society, approximately sixteen million Americans suffer from rosacea, a common skin condition that causes flushing...
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, m...
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion dete...
Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathologi...
Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (g...