NeuroScanAI

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Inspiration: The inspiration for NeuroScanAI came from a shared commitment to improving healthcare through advanced technology. We were moved by the potential to use artificial intelligence to aid in the early detection of brain tumors, which can be life-saving. Our goal is to make MRI-based brain tumor detection more accessible and efficient, ultimately improving patient outcomes and reducing the burden on healthcare professionals.

What it does: NeuroScanAI is an AI solution that utilizes deep learning to analyze MRI images of the brain. It accurately detects and identifies brain tumors in these images, providing critical information for medical professionals and patients. This technology streamlines the diagnosis process, enabling early intervention and improving the chances of successful treatment.

How we built it: We built NeuroScanAI using a deep-learning approach. Initially, we encountered hardware limitations while training the model with EfficientNet. After seeking guidance from a mentor, we transitioned to YOLOv8, which allowed us to train the model while optimizing its performance efficiently. The model was trained on a diverse dataset of brain MRI images, leveraging transfer learning techniques for enhanced accuracy.

Challenges we ran into: One of the significant challenges we faced was hardware limitations during the initial training phase with EfficientNet. To overcome this roadblock, we transitioned to YOLOv8, which was more hardware-efficient and allowed us to proceed with training effectively. Additionally, learning and implementing model deployment posed a unique challenge, requiring us to acquire new skills and knowledge.

Accomplishments that we're proud of: We are proud of successfully developing NeuroScanAI, a powerful tool for early brain tumor detection. Overcoming hardware limitations and adapting our approach to YOLOv8 was a pivotal achievement. We have created an accurate and efficient solution that has the potential to impact the lives of many patients. Additionally, our commitment to continuous learning and development in the field of AI and model deployment is a source of pride.

What we learned: Through the development of NeuroScanAI, we gained valuable experience in training deep learning models, navigating hardware constraints, and transitioning to efficient architectures like YOLOv8. We also acquired the knowledge and skills necessary for deploying AI models, which is crucial for real-world applications.

What's next for NeuroScanAI: The future for NeuroScanAI is promising. We plan to further refine and fine-tune the model to improve its accuracy and robustness. Additionally, we aim to develop a user-friendly interface to make the technology accessible to healthcare professionals and patients. Collaborations with medical institutions and clinics are on the horizon to validate and integrate NeuroScanAI into clinical workflows. Ultimately, our goal is to make early brain tumor detection more accessible, saving lives and improving healthcare outcomes.

Built With

• figma
• flask
• github
• google-colab
• google-docs
• google-drive
• kaggle
• python
• pytorch
• svelte
• sveltekit
• tensorflow