Title:CAD System Design for Two-class Brain Tumor Classification using
Transfer Learning
Volume: 20
Issue: 2
Author(s): Falguni Bhardawaj and Shruti Jain*
Affiliation:
- Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Keywords:
Brain tumor, transfer learning, machine learning, hybrid features, pre-trained models, GLCM and GLDS features.
Abstract:
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.