Convolutional Neural Network-based Framework for Brain Tumor Classification and Segmentation using Magnetic Resonance Images

Summary

Deep learning algorithms were utilized in MRI to perform brain tumor classification and segmentation with U-Net. InceptionV3, DenseNet201, and Inception-ResNet-v2 performed with excellent accuracy on tumor type and grade prediction. GPT-4.0 augmented hybrid models for automatic medical report generation and diagnostic assistance.

Abstract

Early diagnosis of brain tumors is critical for optimization of the prognosis and treatment selection of the patient. Accurate segmentation and categorization of brain tumors are essential to create specialist treatment techniques. As MRI utilization for brain diagnosis increases and computer vision technology also improves, having a good and effective model to identify and categorize tumors based on MRI scans remains challenging. To address this problem, the authors suggested a deep learning-based technique to segment and classify brain tumors from different datasets. Image preprocessing employed nine augmentation methods to enhance model performance. Segmentation of MRI was done by using a U-Net model.

The developed classification model based on InceptionV3 and DenseNet201 predicts the existence of the tumor and categorizes it into Glioma, Meningioma, and Pituitary. With 99.15% accuracy, InceptionV3 is higher than DenseNet201's 98.75% in tumor classification. Additional tumor classification was performed by Clustering as HGG and LGG on the basis of Inception-ResNet-v2. Tumor grades (1-4) are identified with 96.64% accuracy by Inception-ResNet-v2. An autonomous system integrates hybrid models with GPT-4.0 to generate reports. Hence, this novel framework could very well be suitable for clinics when used for automatically identifying and separating brain tumors utilizing input images captured from MRI scans.

Introduction

Brain tumors may significantly impair patients' and families' quality of life and account for one instance of every 100 cancers treated each year in the US^1,2,3. Glioma is the most prevalent primary brain tumor among people in the United States, which has an incidence of 6.5 per 100,000. They arise in astrocytes, oligodendrocytes, and ependymal cells, the glial cells that provide nutrition to the neurons of the brain. Glioma is categorized into different types on the basis of the affected glial cell in the tumor as well as its genetic profile, which may now be useful ....

Protocol

Dataset description and exploratory analysis
The dataset consists of multiple sources to enhance the reliability and accuracy of the model. Merged_dataset contains 20,620 images from Dataset A (3,054), Dataset B (3,264), Dataset C (10,000), and Dataset D (4,292). Further, 1,425 images from the Brad data set were added for Glioma tumor grades (HGG, LGG). This diverse dataset will ensure better generalization, reduce biases, and improve the performance of the model. The large dataset enables comprehensive evaluations and thus, there is a higher probability of getting the right predictions in real-world applications of classification tasks.

Results

The training environment leverages Kaggle's NVIDIA Tesla T4 GPU, facilitating efficient model training. The libraries of importance are TensorFlow, PyTorch, Keras, NumPy, and Pinecone, which facilitate strong deep learning pipelines. DenseNet201, InceptionV3, and Inception-ResNet-v2 were chosen because of their demonstrated efficacy in medical imaging. These designs provide deep feature extraction, robust gradient flow, and hybrid strengths, which improve accuracy, minimize overfitting, and improve model generalization i.......

Discussion

Early diagnosis of brain tumors may be essential to save the life of an individual, as brain tumors can be highly dangerous and fatal. Currently, tumor diagnostics rely on radiologists' manual interpretation, which can cause delays and human error in detecting malignancies in early stages. Hence, this paper introduces a multi-classification brain tumor diagnosis model that can precisely detect, localize, and classify tumors, especially when dealing with varying shapes, sizes, and textures. It combines hybrid methods and .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
fastText	Facebook AI	N/A	Word representation and classification
Google Colab	Google	N/A	Cloud-based Jupyter Notebook environment
Google Colab GPU/TPU	Google	N/A	Cloud-based hardware acceleration
Intel Core i5/i7 or AMD Ryzen 5/7	Intel / AMD	N/A	Processor for local execution (if required)
Matplotlib	Open-source	N/A	Data visualization library
NLTK	Open-source	N/A	Natural Language Toolkit for text processing
NumPy	Open-source	N/A	Numerical computing library
NVIDIA GTX 1650 or Higher (Optional)	NVIDIA	N/A	GPU for deep learning tasks
Pandas	Open-source	N/A	Data manipulation library
Python	Python Software Foundation	N/A	Programming language for ML and NLP
PyTorch	Meta AI	N/A	Deep Learning framework
RAM (8GB Minimum, 16GB Recommended)	Various	N/A	Memory requirement for ML tasks
Scikit-learn	Open-source	N/A	Machine Learning library
Seaborn	Open-source	N/A	Statistical data visualization
SpaCy	Explosion AI	N/A	Industrial-strength NLP library
SSD Storage (256GB Minimum, 512GB Recommended)	Various	N/A	Storage for dataset processing
TensorFlow	Google	N/A	Deep Learning framework