The problem Byte Coders Tumor Prediction solves

This website by Byte Coders is a groundbreaking platform designed to address critical challenges in the field of medical imaging and tumor diagnosis. This innovative website seamlessly integrates advanced machine learning models to provide comprehensive solutions to users:

1. Tumor Recognition: Our platform empowers individuals to effortlessly upload their MRI scans for precise tumor recognition. By analyzing the input patient data, our state-of-the-art machine learning algorithms can accurately detect the presence of a tumor and even determine its specific type, providing crucial information for early diagnosis and treatment planning.

2. Doctor Referral: Accessing medical expertise is made effortless with this website. Users can conveniently find and connect with specialized doctors through our platform. Whether they need a consultation, a second opinion, or ongoing care, it helps bridge the gap between patients and healthcare professionals.

3. Support and Assistance: We understand the importance of support during challenging times. Our platform offers a dedicated support page, ensuring that users can easily reach out to the team for any questions, concerns, or guidance they may need.

The project aims to be a lifeline for those seeking timely and accurate tumor diagnosis, expert medical guidance, and compassionate support. Our cutting-edge technology is driven by a mission to improve healthcare accessibility and enhance the well-being of individuals dealing with tumor-related concerns.

Challenges we ran into

During the development of the website, we encountered a few challenges, all of which were successfully addressed:

1. Precision-Oriented Model: Our primary challenge revolved around creating a classification model that could deliver highly accurate tumor predictions. To overcome this, we prioritized maximizing accuracy and minimizing errors by implementing a Convolutional Neural Network (CNN) model and optimizing it with the ADAM optimizer.

2. Accessibility Enhancement: Ensuring that our platform is accessible to all users was another vital challenge. We worked diligently to integrate images and colors in a way that promotes inclusivity, ensuring that individuals of varying abilities can easily navigate and utilize the website. Our commitment to accessibility reflects our dedication to serving a diverse user base effectively.

3. GitHub restrictions on the number and size of files being uploaded prevented us from loading our train and test dataset into the repository.