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Current Cancer Therapy Reviews


ISSN (Print): 1573-3947
ISSN (Online): 1875-6301

Research Article

CAD System Design for Two-class Brain Tumor Classification using Transfer Learning

Author(s): Falguni Bhardawaj and Shruti Jain*

Volume 20, Issue 2, 2024

Published on: 06 October, 2023

Page: [223 - 232] Pages: 10

DOI: 10.2174/1573394719666230816091316

Price: $65


Background: The occurrence of brain tumors is rapidly increasing, mostly in the younger generation. Tumors can directly destroy all healthy brain cells and spread rapidly to other parts. However, tumor detection and removal still pose a challenge in the field of biomedicine. Early detection and treatment of brain tumors are vital as otherwise can prove to be fatal.

Objective: This paper presents the Computer Aided Diagnostic (CAD) system design for two classification of brain tumors employing the transfer learning technique. The model is validated using machine learning techniques and other datasets.

Methods: Different pre-processing and segmentation techniques were applied to the online dataset. A two-class classification CAD system was designed using pre-trained models namely VGG16, VGG19, Resnet 50, and Inception V3. Later GLDS, GLCM, and hybrid features were extracted which were classified using Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Probabilistic Neural Network (PNN) techniques.

Results: The overall classification accuracy using Inception V3 is observed as 83%. 85% accuracy was obtained using hybrid GLCM and GLDS features using the SVM algorithm. The model has been validated on the BraTs dataset which results in 84.5% and 82% accuracy using GLCM + GLDS + SVM and Inception V3 technique respectively.

Conclusion: 2.9% accuracy improvement was attained while considering GLCM + GLDS + SVM over kNN and PNN. 0.5% and 1.2% accuracy improvement were attained for CAD system design based on GLCM + GLDS + SVM and Inception v3 model respectively.

Keywords: Brain tumor, transfer learning, machine learning, hybrid features, pre-trained models, GLCM and GLDS features.

« Previous
Graphical Abstract
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