Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor
Asim Zaman, Ling Yu, Nasir Ud Din, Kifayat Ullah, Qaisar Hayat
Journal of Engineering Research and Reports · pp. 41–50 · Published 17 Jun 2022
10.9734/jerr/2022/v22i917563Abstract
The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.
References (35)
- 1 ImageNet classification with deep convolutional neural networks [DOI]
- 2 Brain tumor segmentation with Deep Neural Networks [DOI]
- 3 Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images [DOI]
- 4 Proceedings. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition [DOI]
- 5 Simultaneous feature learning and hash coding with deep neural networks [DOI]
- 6 Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme [DOI]
- 7 Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks [DOI]
- 8 From image-level to pixel-level labeling with Convolutional Networks [DOI]
- 9 MRI Segmentation of the Human Brain: Challenges, Methods, and Applications [DOI]
- 10 Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm [DOI]
- 11 A Detailed Review of Feature Extraction in Image Processing Systems [DOI]
- 12 Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification [DOI]
- 13 Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization [DOI]
- 14 Deep-learning based, automated segmentation of macular edema in optical coherence tomography [DOI]
- 15 Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images [DOI]
- 16 CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF [DOI]
- 17 Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network [DOI]
- 18 Segmentation-Aware Convolutional Networks Using Local Attention Masks [DOI]
- 19 A brief review of feed-forward neural networks [DOI]
- 20 MRI brain cancer classification using Support Vector Machine [DOI]
- 21 Combining Tissue Segmentation and Neural Network for Brain Tumor Detection
- 22 Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks [DOI]
- 23 3D discrete wavelet transform‐based feature extraction for hyperspectral face recognition [DOI]
- 24 Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine [DOI]
- 25 Computer aided brain tumor detection with histogram equalization and morphological image processing techniques [DOI]
- 26 Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
- 27 Fast two-step histogram-based image segmentation [DOI]
- 28 The Image Segmentation Techniques [DOI]
- 29 Analysis of dual tree M‐band wavelet transform based features for brain image classification [DOI]
- 30 Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier [DOI]
- 31 Automatic Brain Tumor Detection and Classification of Grades of Astrocytoma [DOI]
- 32 Tumor Disease Diagnosis Model Based on BP Neural Network [DOI]
- 33 Challenges Inherent in Building an Intelligent Paradigm for Tumor Detection Using Machine Learning Algorithms [DOI]
- 34 Color Image Segmentation [DOI]
- 35 Proceedings of IEEE International Conference on Computer Vision [DOI]
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