Research Area: Machine Learning
Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.
Keywords:
Brain tumor
Deep learning
Transfer learning
AlexNet
GoogLeNet
VGG
Figshare dataset
Author(s) Name: Arshia Rehman, Saeeda Naz, Muhammad Imran Razzak, Faiza Akram & Muhammad Imran
Journal name: Circuits, Systems, and Signal Processing
Conferrence name:
Publisher name: Springer Link
DOI: 10.1007/s00034-019-01246-3
Volume Information: 39, pages 757–775 (2020)
