Mustafa Al-Maini
A5079330804- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Artificial Intelligence in Healthcare
- Artificial Intelligence in Healthcare and Education
- Lung Cancer Diagnosis and Treatment
- Machine Learning in Healthcare
- Cardiac Imaging and Diagnostics
- AI in cancer detection
- Advanced X-ray and CT Imaging
- Acute Ischemic Stroke Management
- Cutaneous Melanoma Detection and Management
- Biomarkers in Disease Mechanisms
- COVID-19 Clinical Research Studies
- Phonocardiography and Auscultation Techniques
- Parkinson's Disease Mechanisms and Treatments
- COVID-19 and healthcare impacts
- Retinal Imaging and Analysis
- Streptococcal Infections and Treatments
- Cardiovascular Function and Risk Factors
- Systemic Lupus Erythematosus Research
- Sepsis Diagnosis and Treatment
- Rheumatoid Arthritis Research and Therapies
- Traditional Chinese Medicine Studies
- Retinal and Optic Conditions
- Systemic Sclerosis and Related Diseases
: The price of medical treatment continues to rise due (i) an increasing population; (ii) aging human growth; (iii) disease prevalence; (iv) a in the frequency patients that utilize health care services; and (v) increase price.
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based AI, “COVLIAS 2.0-cXAI” using four kinds class activation maps (CAM) models. Methodology: Our cohort consisted ~6000 CT slices from two sources (Croatia, 80 patients Italy, 15 control patients). COVLIAS 2.0-cXAI design three stages: (i) automated segmentation hybrid deep...
Lung computed tomography (CT) techniques are high-resolution and well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization typically overfitted. Such trained AI practical clinical settings therefore give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to transfer (TL) both non-augmented augmented frameworks.
Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of severity. The process automated challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. methodologies proposed in 2020 were semi- or but not reliable, accurate, user-friendly. study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting hybrid deep learning (HDL) models segmentation. Methodology:...
Background: COVID-19 is a disease with multiple variants, and quickly spreading throughout the world. It crucial to identify patients who are suspected of having early, because vaccine not readily available in certain parts Methodology: Lung computed tomography (CT) imaging can be used diagnose as an alternative RT-PCR test some cases. The occurrence ground-glass opacities lung region characteristic chest CT scans, these daunting locate segment manually. proposed study consists combination...
Skin lesion classification plays a crucial role in dermatology, aiding the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep technique, powerful, novel, generalized method for extracting features skin This technique holds significant promise enhancing diagnostic accuracy by using seven pre-trained TL...
Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention Vision Transformers (ViTs) to enhance disease segmentation classification. We hypothesize that Attention will accuracy ViTs improve classification performance. The methodologies shed light on model decision-making processes,...
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in training stage because only one set ground truth (GT) annotations are evaluated. We propose robust and stable inter-variability analysis CT to avoid effect bias. Methodology: The proposed study consists two GT tracers for chest CT. Three AI models, PSP Net, VGG-SegNet, ResNet-SegNet, were...
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The study compared the Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) tool, where uses hybrid deep learning (HDL)...
The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment carotid plaques. In proposed study, AtheroEdge™ 3.0