BrainWave Diagnosis
Unlocking the Future of Brain Tumor Diagnosis with AI Precision. In this, we've created the models namely the train model and ml model for training and testing the AI And we've also buit the app
Created on 2nd November 2023
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BrainWave Diagnosis
Unlocking the Future of Brain Tumor Diagnosis with AI Precision. In this, we've created the models namely the train model and ml model for training and testing the AI And we've also buit the app
The problem BrainWave Diagnosis solves
"BrainWave Diagnosis: Precision Brain Tumor Identification" aims to tackle a significant problem in the field of neuroimaging and healthcare – the need for swift, accurate, and reliable identification and classification of brain tumors. This problem is multi-faceted, and BrainWave Diagnosis provides a comprehensive solution.
Brain tumors are complex, and their timely diagnosis is crucial for effective treatment. Conventional methods of identifying and classifying brain tumors, while effective to some extent, often face challenges. These challenges include the potential for misdiagnosis, long waiting times for results, and variations in interpretation among medical professionals. These issues can have a profound impact on patient outcomes, as delayed diagnosis and incorrect classification can lead to a poorer prognosis and treatment options.
The advent of artificial intelligence and deep learning has brought about a significant shift in the way we approach medical imaging. BrainWave Diagnosis leverages these technological advancements to revolutionize the process of brain tumor identification.
Here's how BrainWave Diagnosis addresses the problem:
Swift and Accurate Diagnosis: BrainWave Diagnosis is designed to rapidly analyze medical imaging data, including CT scans and MRI images. It can detect even subtle signs of brain tumors, allowing for early diagnosis. This swift analysis is crucial, as early intervention is often key to improving patient outcomes.
The project also shows up the map of the nearest hospital and the report of the patient are sent to their email which can be furthur viewed by the patient and the doctor
Challenges we ran into
Challenges we faced during the project :
Data Quality and Quantity: Acquiring a sufficient amount of high-quality medical imaging data for training and validation can be a substantial challenge. The availability of diverse and well-annotated datasets is crucial for developing accurate AI models.
we had talk with different healthcare officials and even collected the data from the Kaggle for the image preprocessing
User Interface and Usability: Designing an intuitive user interface for medical professionals and ensuring the system is user-friendly can be challenging. It should seamlessly integrate into their workflow and be easy to interpret.
While developing the project there was the issue of UI and how to interact with the Project we took nearly 5-6 days to make it user-friendly
While making the app for the project there were several issues of the app crashes the app for not running and was not able to predict the brain tumor accurately we surfed for the solution iin internet and solved the issue