[HTML][HTML] Diabetic retinopathy detection through deep learning techniques: A review
WL Alyoubi, WM Shalash, MF Abulkhair - Informatics in Medicine Unlocked, 2020 - Elsevier
Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes
lesions on the retina that effect vision. If it is not detected early, it can lead to blindness …
lesions on the retina that effect vision. If it is not detected early, it can lead to blindness …
[HTML][HTML] Deep learning for brain MRI segmentation: state of the art and future directions
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …
and relies on accurate segmentation of structures of interest. Deep learning-based …
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey
D Karaboga, E Kaya - Artificial Intelligence Review, 2019 - Springer
In the structure of ANFIS, there are two different parameter groups: premise and
consequence. Training ANFIS means determination of these parameters using an …
consequence. Training ANFIS means determination of these parameters using an …
A review of explainable and interpretable AI with applications in COVID‐19 imaging
The development of medical imaging artificial intelligence (AI) systems for evaluating COVID‐
19 patients has demonstrated potential for improving clinical decision making and assessing …
19 patients has demonstrated potential for improving clinical decision making and assessing …
[HTML][HTML] MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey
N Yamanakkanavar, JY Choi, B Lee - Sensors, 2020 - mdpi.com
Many neurological diseases and delineating pathological regions have been analyzed, and
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …
A survey of deep-learning applications in ultrasound: Artificial intelligence–powered ultrasound for improving clinical workflow
Ultrasound is the most commonly used imaging modality in clinical practice because it is a
nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time …
nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time …
Artificial intelligence in ultrasound
YT Shen, L Chen, WW Yue, HX Xu - European Journal of Radiology, 2021 - Elsevier
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Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue …
Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue …
A survey on brain tumor detection techniques for MR images
PK Chahal, S Pandey, S Goel - Multimedia Tools and Applications, 2020 - Springer
One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …
An ensemble learning approach for brain cancer detection exploiting radiomic features
Abstract Background and Objective The brain cancer is one of the most aggressive tumour:
the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection …
the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection …
A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image
The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …
and artificial intelligence conspire in the timely delineation, detection, and classification of …