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SAMEH ABDELGANY HAMOUDA

Assistant Professor

SAMEH ABDELGANY HAMOUDA
saabdelwahab@ju.edu.sa
Information Systems - Computer and Information Sciences College
College
Computer and Information Sciences College
كلية علوم الحاسب والمعلومات
Department
Information Systems
نظم المعلومات
Researches
15
15
Page Visits
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Current courses

Recent courses

Fm researches

Last Researches

XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
A Robust EfficientNetV2-S Classifier for Predicting Acute Lymphoblastic Leukemia Based on Cross Validation
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans
SAMEH ABDELGANY ABDELWAHAB HAMOUDA

fm info

Researches

XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
A Robust EfficientNetV2-S Classifier for Predicting Acute Lymphoblastic Leukemia Based on Cross Validation
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
A Robust Tuberculosis Diagnosis Using Chest X-Rays Based on a Hybrid Vision Transformer and Principal Component Analysis
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
An Accurate Deep Learning-Based Computer-Aided Diagnosis System for Gastrointestinal Disease Detection Using Wireless Capsule Endoscopy Image Analysis
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Advanced Deep Learning Fusion Model for Early Multi-Classifcation of Lung and Colon Cancer Using Histopathological Images
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Adaptive Dynamic Learning Rate Optimization Technique for Colorectal Cancer Diagnosis Based on Histopathological Image Using EfficientNet-B0 Deep Learning Model
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
A fully automatic fine tuned deep learning model for knee osteoarthritis detection and progression analysis
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Computer-Aided Diagnosis for Early Signs of Skin Diseases Using Multi Types Feature Fusion Based on a Hybrid Deep Learning Model
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images
SAMEH ABDELGANY ABDELWAHAB HAMOUDA
Diagnosis of various skin cancer lesions based on fine-tuned ResNet50 deep network
SAMEH ABDELGANY ABDELWAHAB HAMOUDA

Projects

Detection and Diagnosis of Patellofemoral Osteoarthritis Disorders using LSTM Recurrent Network Based on sEMG Analysis
2020 - 2023

Knee osteoarthritis (KOA) sufferers have one of the highest disability-adjusted life years. The entire knee
joint is affected by KOA. KOA is a condition that makes it hard for the knee to move normally. In KOA, the
damage to the joints is irreversible, and the only treatment is a total knee replacement (TKR), which is
expensive and only lasts a short time, especially for obese people. The individual’s social isolation and
low quality of life are significant outcomes of KOA. Despite being time-consuming and highly subject

An Early Stage Colon Cancer Diagnosis System Based on Artificial Intelligence Techniques
2021 - 2023

Histopathology is the most accurate method for diagnosing cancer and identifying therapeutic and prognostic targets. Early detection of cancer significantly increases the likelihood of survival. With the remarkable success of deep networks, significant efforts have been made to analyze cancer disorders, particularly colon and lung cancers. To achieve this, this paper explores the efficiency of deep networks in diagnosing various types of cancer using histopathology image processing.

Deep learning- based model for gastrointestinal disease detection using adaptive learning algorithm
2024 - 2024

Peptic ulcers and stomach cancer are the gastrointestinal (GI) tract conditions that occur the most frequently. Wireless Capsule Endoscopy (WCE) is the more widely used and powerful technology in clinical settings as a safe and painless method for diagnosing gastrointestinal diseases. Through WCE, doctors find various irregularities like chronic diarrhea, ulcers, bleeding, polyps, small intestine cancer or tumor, Crohn's disease, or any other invasive gastrointestinal condition.

A Vision Transformer-based CAD System for Tuberculosis Diagnosis
2024 - 2024

This research project presents a CAD system that leverages the use of ViT to precisely detect TB in CXR images. The innovative CAD system allows for the swift and accurate identification of TB, facilitating prompt actions and improving the outlook for patients. Additionally, it streamlines the diagnostic process, resulting in reduced time and costs for individuals while alleviating the burden on healthcare professionals

Computer Aided Diagnosis System for multi blood diseases
2021 - 2023

The immune system’s overproduction of white blood cells (WBCs) results in the most
common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the
primary causes of death worldwide. The most well-known type of leukemia found in the human
bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and
kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early

A Fully Automatic Fine Tuned Deep Learning Model for Colorectal Cancer Detection based on Feature Fusion
2024 - 2024

The high mortality rate of colorectal cancer (CRC) continues to impact human life worldwide. It helps prevent disease and extends human life by being detected early. CRC is frequently diagnosed and detected through histopathological examination. The decision is based on clinicians' subjective perceptions, and based on daily image analyses, the current heavy workload of pathologists in clinics and hospitals may easily result in an unconscious misdiagnosis of the CRC. Moreover, visual inspection of histopathological diagnoses takes more time.

A Deep Learning-Based Computer-Aided Diagnosis System for Accurate Detection of Different Types of Eye Diseases
2024 - 2025

This research project presents a new CAD model for the classification of Eye Diseases. This powerful CAD model aims to assist healthcare professionals in accurately diagnosing Eye Diseases. It has the potential to reduce the workload of primary ophthalmologists, improve early detection, and facilitate appropriate Eye Diseases treatment

Experience

assistant professour
Jouf University
2016 - 2025
Lecturere
mansoura university-egypt
2009 - 2013

Working as a lecturer to carry out teaching and scientific research tasks within Mansoura University

assistant professour
mansoura university
2013 - 2016

Working as an assistant professor to carry out teaching and scientific research tasks within Mansoura University

Certifications

external review for higher eductaional insititutes
2015 - 2025

Education

M.A.
mansoura university - faculty of computer and information
2009 - 2005
Ph.D.
mansoura university - faculty of computer and information
2013 - 2010
BA
mansoura university - faculty of computer and information
2003 - 1999
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