AI in Healthcare Lab
The thesis project was developed here.
Theoretical and practical experience was gained in machine learning and image analysis.
I have recently graduated from Eskişehir Technical University with a degree in Computer Engineering. Additionally, I have completed a minor program in Artificial Intelligence and Machine Learning at Eskişehir Technical University. During my studies, I was a lab student at the AI in Healthcare Lab, where I focused on developing my skills in image processing and machine learning.
I am currently seeking a position in the field of artificial intelligence and machine learning.
Master of Science in Computer Engineering
Sep. 2024 – Current
Eskisehir, Türkiye
Minor in Artificial Intelligence and Machine Learning
Feb. 2021 – Jan. 2024
Eskisehir, Türkiye
Bachelor of Science in Computer Engineering
Sep. 2019 – Jun. 2024
Eskisehir, Türkiye
I have worked on data analysis, model development, and algorithm design using Python. I have carried out projects particularly in the fields of machine learning algorithms, deep learning algorithms and computer vision. My projects span across various domains such as healthcare, finance, and commerce. I have experience working with large datasets, performing data cleaning, transformation, and feature engineering. I have also applied augmentation techniques to balance imbalanced datasets. I am proficient in using popular libraries such as TensorFlow, Keras, and PyTorch for modeling. Additionally, I have experience in model evaluation, hyperparameter optimization, and utilizing explainable AI (XAI) methods to enhance the accuracy and reliability of projects. Furthermore, I have developed end-to-end projects by integrating models into user interfaces using the Gradio library.
The thesis project was developed here.
Theoretical and practical experience was gained in machine learning and image analysis.
Image processing techniques were utilized in the projects.
The VGG16 model within PyTorch was utilized in the Emotion Recognition project.
Object detection was conducted using YOLOv7 and YOLOv8 algorithms.
Segmentation performed using U-Net architecture with ResNet.
In the project, the UI/UX design part was done using Figma.
The web page, designed in Figma, was coded with adherence to SEO rules and utilizing Bootstrap 5.3.
Git was employed for source code review, code sharing, branching structure, and version control management.
The system has three main components: (i) The eye disease prediction model is trained with a dataset containing fundus images with six different diseases. (ii) The diabetic retinopathy severity prediction model is trained with fundus images obtained from patients with diabetic retinopathy labeled with the stage of the disease. (iii) The system locates the regions related to eye diseases using the Grad-CAM.
🏆 The project has been selected as the winner among 31 groups at the 17th Project Fair and Competition held at Eskisehir Technical University on May 28, 2024.
🎓 This research was presented at the Engineering Sciences and Research Student Congress, Ankara, Atilim University, 2024.