A5053278966- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Prostate Cancer Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Advanced Neural Network Applications
- Anomaly Detection Techniques and Applications
- Medical Imaging and Analysis
- Brain Tumor Detection and Classification
- Medical Image Segmentation Techniques
- MRI in cancer diagnosis
- Context-Aware Activity Recognition Systems
- Imbalanced Data Classification Techniques
- IoT and Edge/Fog Computing
Mansoura University
2021-2024
University of Louisville
2019-2023
Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions reduce the dependence on invasive techniques. In this study, a CAD system that detects identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: proposed first uses non-negative matrix factorization (NMF) to integrate three different types features for accurate segmentation regions. Then, discriminatory in form apparent diffusion coefficient...
The widespread use of credit cards has led to an increase in fraud. Credit card fraud detection involves identifying and preventing fraudulent transactions, either real-time or post-occurrence. This paper seeks create advanced model via data mining. proposed method comprises four essential steps: acquisition, preprocessing, feature selection, detection. A recent balanced dataset is acquired, containing 28 anonymized features about the along with transaction amount label (normal fraud). then...
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome 2 (SARS-COV-2), surprised the world in December and has threatened lives of millions people. Countries all over closed worship places shops, prevented gatherings, implemented curfews to stand against spread COVID-19. Deep Learning (DL) Artificial Intelligence (AI) can have a great role detecting fighting this disease. learning be used detect COVID-19 symptoms signs from different imaging modalities,...
This paper proposes a computer-aided diagnosis (CAD) system for localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI). uses DW-MRI data sets that were acquired at four b-values: 100, 200, 300, and 400 smm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . The first step in the proposed is segmentation using level set method. evolution of this guided not only by intensity voxels but also shape prior...