Medical image segmentation plays a critical role in clinical diagnosis and treatment planning; however, the performance of deep learning models typically depends on large quantities of pixel-wise annotated data, which are costly and time-consuming to obtain. T…
This research represents a hybrid model for polyp segmentation in medical images, which combines convolutional neural networks (CNN) and vision transformers (ViT). We propose a hybrid architecture that integrates UNet with a Vision Transformer, where UNet is u…
Pneumonia is a leading global cause of mortality, claiming approximately 2.5 million lives an-nually and placing exceptional diagnostic pressure on radiologists in resource-limited settings. Manual interpretation of chest X-ray (CXR) images is time-consuming,…
This research project addresses the significant challenge of class imbalance in medical deep learning classification models, particularly when the abnormal class is underrepresented. Class imbalance can severely affect the classification performance of deep le…
Recent advancements in Computer Vision (CV) and Machine Learning (ML) have fundamentally revolutionized the field of human behaviour understanding, enabling intelligent systems to move beyond simple motion detection to the interpretation of complex, semantic a…
The study explores the design and execution viability of deep learning algorithms to the automated system of identifying plant diseases using leaf images.The global agricultural sector is experiencing significant losses in crop production due to late or inappr…
Vision-Language Models (VLMs) have revolutionized open-vocabulary recognition, yet adapting them to specialized domains under data-scarce conditions remains a significant challenge. This thesis investigates efficient strategies to bridge the gap between induct…
The goal of this thesis is to solve the significant problem of inter-observer variability-induced diagnostic delays by presenting a sophisticated deep learning system for automated brain tumor diagnosis utilizing MRI imagery. Pituitary adenoma, extra-axial men…
The increasing adoption of machine learning (ML) and artificial intelligence (AI) in critical decision-making has intensified demands for explainability and transparency, particularly in high-risk domains such as finance. This dissertation investigates how exp…
Psoriasis is a chronic inflammatory skin disease characterized by the presence of lesions that vary in shape, size, and color. Accurate segmentation of these lesions from clinical images is crucial for effective diagnosis and treatment monitoring. However, the…
Pneumonia continues to be a major global health threat and one of the leading causes of mortality, with chest X-rays serving as the primary diagnostic tool. Despite their promise, deep learning models for pneumonia detection often face limitations in generaliz…
Federated Learning (FL) enables multiple parties to collaboratively train a shared model without exposing raw data. However, FL often suffers from degraded performance due to three core challenges: non-IID data distributions, residual privacy risks, and limite…
أنا مساعد المستودع. اسألني عن الرسائل والأطاريح حسب الموضوع أو الجامعة أو الدولة أو السنة أو الدرجة، أو اطلب أعداداً وإحصاءات.
يجيب من قاعدة بيانات المستودع فقط، ولا يخترع سجلات أو أرقاماً. لا يُحفظ ما تكتبه على الخادم.
قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
