The problem NeuroDetect solves

Brain MRI interpretation is a high-stakes, time-intensive task that requires expert attention and experience. With the growing number of MRI scans and a shortage of trained radiologists—especially in India and other resource-constrained regions—diagnostic delays are common. These delays can significantly impact patient outcomes, particularly in brain tumor cases where early detection is critical.

While AI models can assist in image analysis, many existing solutions act as black boxes, providing predictions without explanations. This makes them difficult to trust and risky to use in real clinical workflows.

NeuroDetect solves this by providing explainable, decision-support AI.
It does not replace doctors or automate diagnoses. Instead, it helps clinicians and medical students by:

Highlighting suspicious regions in MRI scans

Providing confidence scores to communicate uncertainty

Explaining why a prediction was made using visual heatmaps

This makes MRI analysis faster, safer, and more transparent, while keeping human judgment at the center.

Challenges I ran into

One of the biggest challenges was integrating a trained deep learning model into a real-time backend system. Model serialization, managing inference latency, and ensuring compatibility during deployment required careful handling.

Another major hurdle was deploying both the backend and frontend as a cloud-based system. We faced issues related to CORS policies, API request handling, and performance optimization under limited resources.

Additionally, balancing explainability, ethical considerations, and user experience was challenging. In medical AI, it is not enough for a model to be accurate—it must also be understandable and responsibly designed. We overcame these challenges through iterative testing, simplifying model pipelines, and clearly defining the system as a human-in-the-loop assistant, not an autonomous decision-maker.