Research On Brain Tumor Detection
Researchers developed a deep learning model using MRI images to classify brain tumors into four types: Glioma, Meningioma, Pituitary, and No Tumor. The goal is to create an accurate diagnosis tool for medical experts.
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Deployment & Code
Project Snapshot
Core Stack
DL
Fine-Tuning
Transfer Learning
VGG16
Overview
This study 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 proposed work, 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).