Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement
Road accidents remain a major global concern, with driver drowsiness and delayed reaction times recognized as key contributing factors. This thesis advances driver-monitoring research by developing multimodal approaches that integrate electroencephalography (E…
Exploring Multimodal Deep Learning Approaches for Coronary Artery Disease Diagnosis
Coronary Artery Disease (CAD) is a prevalent cause of death in the modern world, with about 20 million victims every year. Limited annotated data, inter-observer variability, and the intrinsic complexity of angiographic imaging challenge its diagnosis. This th…
Deep learning and natural language processing for computer aided diagnosis
Multimodal artificial intelligence (AI) is a cutting-edge technique that integrates diverse modalities, such as imaging and textual data, to enhance classification and regression tasks. This dissertation focuses on the integration, comparison, and evaluation o…
Advancing Computational Pathology with Multimodal Deep Learning
Histopathological assessment of tissue biopsies remains the cornerstone of cancer diagnosis, yet it is limited by diagnostic subjectivity and an incomplete view of the molecular heterogeneity that drives outcomes. This dissertation presents a suite of artifici…
Improving Model Generalization of Pneumonia Detection from Chest Xray Images Using Deep Learning and Transfer Learning
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…
Deep Learning-Based Medical Image Analysis for Enhanced Diagnosis and Severity Assessment of Viral Pneumonia
Viral pneumonia remains a critical global health concern due to its potential for rapid transmission and widespread outbreaks, as highlighted by the COVID-19 pandemic. Timely and accurate diagnosis, severity assessment, and prognosis are essential for effectiv…
CNN-Based Deep Learning Models for Automated Tuberculosis Detection from Chest X-rays
Tuberculosis (TB) remains one of the leading health concerns of the world especially in the low-resource regions where radiologists who can help one with diagnosis are scarce. Although chest X-ray imaging is prevailing examinations used in the screening of TB,…
Applied deep learning for human action recognition in video surveillance, healthcare monitoring, and robotics
Human behavior recognition is a cutting-edge field of artificial intelligence focused on interpreting and understanding human actions, gestures and activities through advanced computational algorithms and machine learning techniques. These algorithms serve as…
Deep Learning-Based Perception Systems for Advancing Autonomous Driving and Road Safety
Deep Learning Based Perception on Radar Data for Autonomous Driving
This thesis investigates perception tasks using automotive radar sensors for autonomous driving. Compared to cameras and lidars, radar sensors offer unique advantages including weather robustness and direct velocity measurements. We specifically focus on lever…
Development of deep learning based perception module for autonomous driving
Otonom araçlar, günümüzde güvenli, konforlu ve sürdürülebilir ulaşım çözümleri sunma potansiyeli sayesinde hızla gelişmekte ve geleceğin mobilite vizyonunu şekillendirmektedir. Otonom araçların temelini oluşturan yazılım ve donanım mimarisi; sensör donanımları…
