An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network
Open Access
- 8 March 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Complex & Intelligent Systems
- Vol. 7 (4), 2023-2036
- https://doi.org/10.1007/s40747-021-00310-3
Abstract
Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.Keywords
This publication has 62 references indexed in Scilit:
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI ImagesIEEE Transactions on Medical Imaging, 2016
- Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural NetworksComputational and Mathematical Methods in Medicine, 2015
- Within-brain classification for brain tumor segmentationInternational Journal of Computer Assisted Radiology and Surgery, 2015
- Detection of Alzheimer’s disease by displacement field and machine learningPeerJ, 2015
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)IEEE Transactions on Medical Imaging, 2014
- The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and CollaborationJournal of Medical Internet Research, 2013
- State of the art survey on MRI brain tumor segmentationMagnetic Resonance Imaging, 2013
- Brain TumorsThe New England Journal of Medicine, 2001
- A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple BoundsSIAM Journal on Numerical Analysis, 1991
- A new method for gray-level picture thresholding using the entropy of the histogramComputer Vision, Graphics, and Image Processing, 1985