This project aimed to detect brain tumors from MRI images using a TensorFlow-Keras implementation of the VGG19 architecture. Leveraging a dataset of 10,000 augmented MRI images, the model achieved an accuracy exceeding 80%.
Developed a full-stack Next.js application that enables healthcare providers to upload medical records for AI-generated summary reports. Integrated OpenAI’s GPT API to analyze patient vitals and identify serious abnormalities, allowing doctors to quickly access essential patient information.
Sell-Easy an innovative platform developed to facilitate the buying and selling of pre-owned items within the campus community. With a focus on sustainability and affordability, Sell-Easy provides students with a convenient way to exchange their old belongings, ranging from textbooks and electronics to clothing and furniture.
The VIT Common Slot Finder is a project that employs computer vision to analyze complex student timetables and identify available free slots. Designed for internal VIT student teams, it facilitates meeting scheduling by analyzing the timetables of approximately 100 students to find common free slots.
Counsailia is an AI-driven counseling service designed to connect students with counselors who are their seniors or individuals who have walked a similar path. It starts with a comprehensive questionnaire that tracks students' interests and assesses their current standing.
The CLI was developed for efficient data preprocessing, crucial in refining raw data for analytical tasks. Through a user-friendly command-line tool, it streamlines tasks such as cleaning, encoding, and visualization, thereby empowering data scientists to optimize workflows and ensure data integrity.