A5026423567- AI in cancer detection
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare
- Autism Spectrum Disorder Research
- Efficiency Analysis Using DEA
- Radiomics and Machine Learning in Medical Imaging
- Economic and Environmental Valuation
- Colorectal Cancer Screening and Detection
- Digital Media Forensic Detection
- Poxvirus research and outbreaks
- Bacillus and Francisella bacterial research
University of Oklahoma
2020-2024
The limitations and high false-negative rates (30%) of COVID-19 test kits have been a prominent challenge during the 2020 coronavirus pandemic. Manufacturing those performing tests require extensive resources time. Recent studies show that radiological images like chest X-rays can offer more efficient solution faster initial screening patients. In this study, we develop diagnosis model using Multilayer Perceptron Convolutional Neural Network (MLP-CNN) for mixed-data (numerical/categorical...
The Monkeypox outbreak has emerged as a pressing global health challenge, evidenced by rising cases across nations. Individuals afflicted exhibit diverse dermatological symptoms that risk further transmission via contamination. Our study assessed the efficacy of three modified transfer learning models (M-VGG16, M-ResNet50, M-ResNet101) alongside vision transformers (ViT) four investigations. We achieved high accuracy in discriminating cases, with M-VGG16 achieving 88%, 76%, and 77% Studies...
The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the image dataset. As part of approach, applies pre-processing synthesizing trained model is subsequently evaluated on independent test set in order to assess performance matrices various approaches. results reveal that proposed simultaneously...
Purpose The main objective of the paper is to develop an investment model using data envelopment analysis (DEA) that provides a decision-making framework allocate resources efficiently, such relative efficiency improved within available budget. Design/methodology/approach Firstly, DEA models are used evaluate departments their peers and providing benchmarks for less efficient departments. Secondly, inefficiencies in identified. Finally, departments, decision-support system introduced...
Background: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and possibility that patients will survive. Objective: This study aims investigate feasibility identifying applying a new feature extraction technique predict survival patients. Methods: A retrospective dataset including computed tomography (CT) images 135 was assembled. Among them, 68 survived longer than three years. Several sets radiomics features were extracted incorporated into...