A5066303253- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Digital Imaging for Blood Diseases
- Retinal Imaging and Analysis
- COVID-19 diagnosis using AI
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Brain Tumor Detection and Classification
- Anomaly Detection Techniques and Applications
- Mental Health via Writing
- Long-Term Effects of COVID-19
- Handwritten Text Recognition Techniques
- Renal and related cancers
- Human Pose and Action Recognition
- Cutaneous Melanoma Detection and Management
- Bone Tumor Diagnosis and Treatments
- Medical Imaging and Analysis
- Artificial Intelligence in Healthcare
- Nonmelanoma Skin Cancer Studies
- Prostate Cancer Diagnosis and Treatment
- Infrared Thermography in Medicine
- Advanced X-ray and CT Imaging
- MRI in cancer diagnosis
- Vehicle License Plate Recognition
- Soft tissue tumor case studies
University of Louisville
2022-2025
Mansoura University
2020-2024
Abstract Skin cancer affects the lives of millions people every year, as it is considered most popular form cancer. In USA alone, approximately three and a half million are diagnosed with skin annually. The survival rate diminishes steeply progresses. Despite this, an expensive difficult procedure to discover this type in early stages. study, threshold-based automatic approach for detection, classification, segmentation utilizing meta-heuristic optimizer named sparrow search algorithm...
Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence medical imaging has enabled remarkable developments. The presented framework classifies patients with brain high accuracy efficiency using CNN, pre-trained models, the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray MRI images. Additionally, CNN Transfer Learning (TL) hyperparameters will optimized through MRFO, resulting in improved performance of model. Two public...
Abstract More than 5% of the people around world are deaf and have severe difficulties in communicating with normal according to World Health Organization (WHO). They face a real challenge express anything without an interpreter for their signs. Nowadays, there lot studies related Sign Language Recognition (SLR) that aims reduce this gap between as it can replace need interpreter. However, challenges facing sign recognition systems such low accuracy, complicated gestures, high-level noise,...
Abstract Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD help to relieve the delay progression. However, this very challenging due similarities between other diseases. The current study proposes generic framework for using handwritten images (or) speech signals. For handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila...
Abstract Breast cancer is among the major frequent types of worldwide, causing a significant death rate every year. It second most prevalent malignancy in Egypt. With increasing number new cases, it vital to diagnose breast its early phases avoid serious complications and deaths. Therefore, routine screening important. current evolution deep learning, medical imaging became one interesting fields. The purpose work suggest hybrid framework for both classification segmentation scans. consists...
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces groundbreaking empirical framework designed to revolutionize accurate automatic classification using microscopic histopathology slide images. innovative system capitalizes on power...
Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which anywhere from 1 to 15% global population and thus; considered one leading causes chronic (CKD). In addition renal cancer is tenth most prevalent type cancer, accounting for 2.5% all cancers. Artificial intelligence (AI) in medical systems can assist radiologists other healthcare professionals diagnosing different (RD) with high reliability. This study proposes...
Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of multimodal, including surgery, chemotherapy, occasionally, radiation therapy. Preoperative chemotherapy used routinely European studies select indications North American trials. The objective this study was to build a novel computer-aided prediction system preoperative response tumors. A total 63 patients (age range: 6 months-14 years) were included study, after...
Abstract Prostate cancer is the one of most dominant among males. It represents leading death causes worldwide. Due to current evolution artificial intelligence in medical imaging, deep learning has been successfully applied diseases diagnosis. However, recent studies prostate classification suffers from either low accuracy or lack data. Therefore, present work introduces a hybrid framework for early and accurate segmentation using learning. The proposed consists two stages, namely stage...
Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely...
Human action recognition techniques have gained significant attention among next-generation technologies due to their specific features and high capability inspect video sequences understand human actions. As a result, many fields benefited from techniques. Deep learning played primary role in approaches recognition. The new era of is spreading by transfer learning. Accordingly, this study's main objective propose framework with three phases for are pre-training, preprocessing, This presents...
Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due the scarcity testing kits, especially in developing countries, there a need rely on alternative diagnosis methods. Deep learning architectures built image modalities can speed up pneumonia classification from other types pneumonia. The transfer approach better suited automatically detect cases due limited availability medical images. This paper introduces an Optimized Transfer Learning-based Approach...
Alzheimer's disease (AD) is a chronic that affects the elderly. There are many different types of dementia, but one leading causes death. AD brain disorder leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, or personality changes, and ultimately death due dementia. Unfortunately, no cure has yet been developed for it, it known causes. Clinically, imaging tools can aid in diagnosis, deep learning recently emerged as an important...
Abstract Cardiovascular diseases (CVD) are the most widely spread all over world among common chronic diseases. CVD represents one of main causes morbidity and mortality. Therefore, it is vital to accurately detect existence heart help save patient life prescribe a suitable treatment. The current evolution in artificial intelligence plays an important role helping physicians diagnose different In present work, hybrid framework for detection using medical voice records suggested. A that...
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast survival chances can be improved by early detection diagnosis. For medical image analyzers, diagnosing tough, time-consuming, routine, repetitive. Medical analysis could useful method for detecting such Recently, artificial intelligence technology has been utilized help radiologists identify more rapidly reliably. Convolutional neural networks, among other technologies, are promising...
Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency subjectivity. The advent machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into comprehensive analysis various algorithms, emphasizing their mathematical underpinnings applicability in...
Automatic grading requires the adaption of latest technologies. It has become essential especially when most courses became online (MOOCs). The objectives current work are (1) Reviewing literature on text semantic similarity and automatic exam correction systems, (2) Proposing an framework (HMB-AECF) for MCQs, essays, equations that is abstracted into five layers, (3) Suggesting checker algorithm named “HMB-MMS-EMA”, (4) Presenting expression matching dataset “HMB-EMD-v1”, (5) Comparing...