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PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI

Taha Muezzinoglu¹, Nursena Baygin², Ilknur Tuncer³

¹Department of Computer Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey.

Journal of Digital Imaging

|February 16, 2023

Summary

This summary is machine-generated.

A novel PatchResNet model enhances brain tumor classification using convolutional neural networks (CNNs) and deep feature engineering. This automated framework achieves high accuracy, aiding medical professionals in diagnosis.

Keywords:

Biomedical engineering Brain image classification PatchResNet Transfer learning Tumor classification

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Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Convolutional Neural Networks (CNNs) are pivotal in modern computer vision for image classification.
Automated brain tumor classification models utilizing CNNs are crucial for assisting medical professionals.

Purpose of the Study:

To enhance brain tumor classification performance using CNNs.
To introduce a patch-based deep feature engineering model for improved accuracy.

Main Methods:

A patch-based deep feature engineering model (PatchResNet) was developed using variable-sized patches (32x32, 56x56, 112x112).
Features were extracted from two layers of the pre-trained ResNet50 model.
Feature selection was performed using Neighborhood Component Analysis (NCA), Chi2, and ReliefF, yielding 18 feature vectors.
Classification was conducted using k-nearest neighbors (kNN), with results aggregated via iterative hard majority voting (IHMV).

Main Results:

The PatchResNet model achieved a classification accuracy of 98.10% on a public brain tumor dataset.
The framework demonstrated high performance in classifying four brain tissue types: glioblastoma multiforme (GBM), meningioma, pituitary tumor, and healthy tissue.

Conclusions:

The proposed PatchResNet model offers a self-organized framework for high-performance image classification.
This automated approach effectively selects optimal prediction vectors, contributing to accurate brain tumor diagnosis.