ekinfiliza2242
I am Ekin Filiz Ataş. I just graduated from Information Systems Engineering in Turkey. My previous internships and my current job were generally in the field of UX/UI because I wanted to improve myself in this field and climbed the steps accordingly.
My first internship was a UI Developer position in a good company in Turkey. It was an internship where I was introduced to Vue.js, html, css and javascript and was thrown into realtime projects and I’m glad it happened that way. Software test engineering at Aselsan was my second compulsory internship. In my senior year at the university, the professor deemed my studies and knowledge sufficient and took me as an assistant student for the Python and Django course. I helped many students, even when the professor was stuck, I somehow found solutions and solved the problems. During this process, I found an internship in Germany with my own means, which is a difficult thing for a young person living in Turkey. I was introduced to WordPress during my 3-month internship and made many designs. For my final
year graduation project, I developed a website that can detect whether there is a brain tumor from MRI images uploaded by users, using tools such as Python, Django, CNN, machine learning, Keras, and presents this to the user, and has many other features.
As I was about to graduate from university, I applied to the job opening for a volunteer UX Designer in Switzerland. When I showed the founder the areas I had done and wanted to improve on, he thought that being in the software team and working as a UX Developer would improve me and that I had the necessary competencies to move the startup forward.
Language
Work Experience
Education
Qualifications
Industry Experience
The goal of this research project is to combine Convolutional Neural Networks (CNN) and machine learning to create an easy-to-use guide website that will help people identify brain tumors in the early stages. The website lets people submit their MRI pictures for examination; it is built with Django and Python. Even if the approach yields suggestive results, it is crucial to highlight its low degree of certainty, hence highlighting the need of seeking expert medical advice for precise diagnosis.
The website has a number of educational features, such as a contribution page to assist organizations linked to brain cancer, a comprehensive overview of the disease, a symptom checker that connects users with appropriate medical specialists, and instructional courses on the disease. User data is protected by security measures, and secure access to personalized features is guaranteed by a strong authentication system.
The CNN-based brain tumor detection model, which allows users to get predictions about the likelihood of a tumor in MRI images they submit, is the project’s central component. The machine learning model’s architecture is explained, providing insight into its preprocessing techniques, training procedure, and use of datasets.
In addition to offering a useful tool for early brain tumor diagnosis, this research advances medical technology by addressing the larger issue of brain cancer awareness, education, and support. By taking a user-centric approach, the website seeks to educate people and promotes seeking medical advice for precise diagnosis and treatment.
Keywords: Web Application, Machine Learning, Self-Diagnosis