Brain Tumour Identification
Brain Tumor Classification Using CNN for Medical Imaging explores the power of deep learning in transforming medical diagnostics. By leveraging a custom Convolutional Neural Network, this project enables precise classification of brain tumors from MRI scans, offering a faster, more accurate approach to early detection and clinical decision-making.
Details
AI/ML Engineer
Medical Imaging Analyst
AI Diagnostics
Image Analysis
Industry:
Aritificial Intelligence
Healthcare
Jul. 2024 - Sep. 2024
Overview
This project leverages a custom Convolutional Neural Network (CNN) to classify brain tumors from MRI scans into four categories: Glioma, Meningioma, Pituitary, and No Tumor. Designed to assist in early and accurate diagnosis, the system combines deep learning techniques with medical imaging to enhance clinical decision-making. With a focus on precision and efficiency, the model contributes to improving healthcare outcomes through AI-driven diagnostics.
Key Features
Custom CNN Architecture
Tailored convolutional neural network designed for high accuracy in tumor classification.Multi-Class Detection
Accurately identifies and classifies MRI scans into Glioma, Meningioma, Pituitary, and No Tumor.Advanced Image Preprocessing
Utilizes normalization, data augmentation, and noise reduction for enhanced model performance.High Diagnostic Accuracy
Delivers reliable results to support early medical diagnosis and treatment planning.Scalable & Adaptable
Built to adapt to various medical imaging datasets with potential for real-world clinical integration.
Mission
To develop an AI-driven brain tumor classification system that enhances the accuracy and speed of medical diagnosis, empowering healthcare professionals with reliable tools to improve patient outcomes through innovative deep learning techniques.
Impact
By enabling early and precise detection of brain tumors from MRI scans, this project aims to revolutionize medical imaging diagnostics, reduce diagnostic errors, and support timely treatment decisions, ultimately contributing to better healthcare and improved quality of life for patients.
