Neuro-Scope

Neuro-Scope: Smart Brain Tumor Analysis is an AI-driven system designed to analyze MRI scans and detect brain tumors, providing accurate classification and segmentation. The project’s primary aim is to assist healthcare professionals by enhancing the early detection of brain tumors, which can significantly improve patient outcomes. By automating the analysis of MRI scans, Neuro-Scope can help doctors identify tumor locations, types, and stages more efficiently and accurately.
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Neuroscope: AI-Powered Brain Tumor Analysis System

Neuroscope is an advanced AI-driven medical imaging tool designed for comprehensive brain tumor analysis using MRI scans. It integrates cutting-edge deep learning techniques to enhance the accuracy and efficiency of tumor diagnosis. The system follows a structured, modular workflow to analyze brain tumors at multiple levels, from detection to staging.

Key Features & Workflow:

Tumor Localization (Segmentation)

Neuroscope accurately identifies and segments tumor regions in MRI scans using advanced image processing and deep learning segmentation techniques.

The model distinguishes between healthy and abnormal tissues, providing a precise tumor boundary map.

Tumor Classification (Type & Nature)

Once the tumor is localized, Neuroscope classifies it into four categories: glioma, meningioma, pituitary tumor, or no tumor.

It also differentiates between benign and malignant tumors, aiding in early diagnosis and treatment planning.

Tumor Staging & Feature Extraction

The system assesses tumor progression by categorizing it into initial, intermediate, or advanced stages.

Key parameters, such as tumor size, shape, intensity, and surrounding tissue impact, are extracted for deeper analysis, mimicking the detailed evaluation performed by radiologists.

3D Tumor Visualization (Upcoming Feature)

A future enhancement will introduce 3D tumor modeling, enabling interactive visualization of the tumor’s size, shape, and depth within the brain.

This feature will aid medical professionals in surgical planning and treatment assessment.

Technical Stack & Implementation

Machine Learning & Deep Learning: Convolutional Neural Networks (CNNs), U-Net for segmentation

Data Handling: Uses real-time data set with properly labeled MRI scans and masks

Backend & UI: Python (Flask/FastAPI) for the backend, with a simple web interface for MRI uploads and result visualization

Deployment: Cloud or local server-based execution for scalability

Impact & Goals

Enhanced diagnostic accuracy, reducing false positives/negatives in tumor detection

Faster analysis, aiding doctors in real-time decision-making

Improved patient care with precise tumor classification and staging insights

Neuroscope is designed to bridge the gap between AI-driven automation and medical expertise, offering a powerful diagnostic assistant for radiologists and neurosurgeons.

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Sandeep Reddy
Sandeep Reddy
sandeep_reddy
Shrisha Sriharsha
Shrisha Sriharsha
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Thanish Seemakurthi
Thanish Seemakurthi
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Monish Samsani
Monish Samsani
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