Improving Brain Tumor Segmentation with Data Augmentation Strategies

Authors

Radhika Malhotra
Dept of ECE Dr. B R Ambedkar NIT Jalandhar Jalandhar, India
Jasleen Saini
Dept of CSE UIET, Panjab University Chandigarh, India
Barjinder Singh Saini
Dept of ECE Dr. B R Ambedkar NIT Jalandhar Jalandhar, India
Savita Gupta
Dept of CSE UIET, Panjab University Chandigarh, India

Synopsis

In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.

WREC21
Published
September 22, 2021
Online ISSN
2582-3922