Brain Tumor Detection App
A deep learning-powered web application for automatic brain tumor classification from MRI images. Built with Flask, TensorFlow/Keras, and deployed on Render.
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Deployment & Code
Project Snapshot
Core Stack
DL
Transfer Learning
TensorFlow/Keras
Render
Python
Overview
This project demonstrates a CNN-based approach for categorizing and detecting brain tumors. The dataset utilized in this study comprises four categories: glioma, meningioma, pituitary, and no tumor in a total of 7023 MRI images. In this project, the combination of a pre-trained model (VGG16) and fine-tuning works very accurately to classify the malignant tumor. However, there are three fully connected layers 256, 128, and 64 with dropout and batch normalization respectively, which enhances the overall validation accuracy of 98.67%, and also improves the precision, sensitivity, and specificity rather than commonly used convolutional neural network models (CNNs).