(Publisher of Peer Reviewed Open Access Journals)
Navigation

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-85 December-2021
Full-Text PDF
Paper Title : Segmentation and classification of brain tumor images using statistical feature extraction and deep neural networks
Author Name : Gaurish Joshi, Rajeev Kumar and Amit Kumar Singh Chauhan
Abstract :

Cancer happens to be the second leading cause of death globally. One of the deadliest types of cancers with a very high mortality rate is brain cancer, which is characterized by malignant tumors or neoplasm. The general approach to diagnosis is the analysis of brain image scans. However, mere manual inspection of the images can be non-conclusive, and the diagnosis may need to be preceded by a biopsy, after a surgery. The entire process of prognosis and a biopsy may result in the loss of valuable time and advancement of cancer. Hence, automated tools which can analyze brain scan images and classify them may aid the prognosis and speed up the actual course of action. This paper presents an approach based on segmentation, statistical feature extraction and classification based on deep neural networks. The images used in this work is brain Magnetic Resonance Imaging (MRI) scans. The two-dimensional discrete wavelet transform has been employed for denoising the raw data, followed by statistical feature extraction. Two deep neural network architecture has been proposed which are the Deep Bayes Network (DBN) and the Residual Network (ResNet). The performance of the deep neural network has been evaluated in terms of classification accuracy. The classification of brain MRI images has been done in three categories of images which are meningioma, glioma and pituitary tumors. It has been shown that the proposed method attains an average classification accuracy of 98.13% and 96.89 using DBN and the ResNet respectively, for the used dataset. A comparative analysis with existing work shows improvement in classification accuracy with respect to existing techniques.
Keywords : Brain tumor classification, Discrete wavelet transform, Statistical feature extraction, Deep bayes net, Residual network (ResNet-50), Classification accuracy.
Cite this article : Joshi G, Kumar R, Chauhan AK. Segmentation and classification of brain tumor images using statistical feature extraction and deep neural networks. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(85):1585-1602. DOI:10.19101/IJATEE.2021.874608.
References :
[1]https://www.who.int/health-topics/cancer#tab=tab_1. Accessed 21 July 2021.
[2]https://seer.cancer.gov/data-software/documentation/seerstat/nov2017/. Accessed 21 July 2021.
[3]Roy PK, Bhui S. Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem. International Journal of Electrical Power & Energy Systems. 2013; 53:937-48.
[Crossref] [Google Scholar]
[4]Comoglio PM, Trusolino L, Boccaccio C. Known and novel roles of the MET oncogene in cancer: a coherent approach to targeted therapy. Nature Reviews Cancer. 2018; 18(6):341-58.
[Crossref] [Google Scholar]
[5]Wani AA, Wani MA, Ramzan AU, Nizami FA, Malik NK, Shafiq S, et al. Combination of needle aspiration and core needle biopsy: a new technique of stereotactic biopsy. Asian Journal of Neurosurgery. 2016; 11(2):94-7.
[Crossref] [Google Scholar]
[6]Iqbal S, Khan MU, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomedical Engineering Letters. 2018; 8(1):5-28.
[Crossref] [Google Scholar]
[7]Park C, Took CC, Seong JK. Machine learning in biomedical engineering. Biomedical Engineering Letters. 2018; 8(1):1-3.
[Crossref] [Google Scholar]
[8]Imani M, Ghassemian H. An overview on spectral and spatial information fusion for hyperspectral image classification: current trends and challenges. Information Fusion. 2020; 59:59-83.
[Crossref] [Google Scholar]
[9]Ayadi W, Elhamzi W, Charfi I, Atri M. Deep CNN for brain tumor classification. Neural Processing Letters. 2021; 53(1):671-700.
[Crossref] [Google Scholar]
[10]Arunkumar N, Mohammed MA, Abd GMK, Ibrahim DA, Abdulhay E, Ramirez-gonzalez G, et al. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing. 2019; 23(19):9083-96.
[Crossref] [Google Scholar]
[11]Kumari N, Saxena S. Review of brain tumor segmentation and classification. In international conference on current trends towards converging technologies 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[12]Ajai AR, Gopalan S. Analysis of active contours without edge-based segmentation technique for brain tumor classification using SVM and KNN classifiers. In advances in communication systems and networks 2020 (pp. 1-10). Springer, Singapore.
[Crossref] [Google Scholar]
[13]Song G, Huang Z, Zhao Y, Zhao X, Liu Y, Bao M, et al. A noninvasive system for the automatic detection of gliomas based on hybrid features and PSO-KSVM. IEEE Access. 2019; 7:13842-55.
[Crossref] [Google Scholar]
[14]Jabber B, Rajesh K, Haritha D, Basha CZ, Parveen SN. An intelligent system for classification of brain tumours with GLCM and back propagation neural network. In international conference on electronics, communication and aerospace technology (ICECA) 2020 (pp. 21-5). IEEE.
[Crossref] [Google Scholar]
[15]Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine. 2019.
[Crossref] [Google Scholar]
[16]Kaldera HN, Gunasekara SR, Dissanayake MB. Brain tumor classification and segmentation using faster R-CNN. In advances in science and engineering technology international conferences 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[17]Aarthi R, Prabha KH. Classification of brain neoplasm from multi-modality MRI with the aid of ANFIS classifier. Multidimensional Systems and Signal Processing. 2021; 32(3):933-57.
[Crossref] [Google Scholar]
[18]Krishnammal PM, Raja SS. Medical image segmentation using fast discrete curvelet transform and classification methods for MRI brain images. Multimedia Tools and Applications. 2020; 79(15):10099-122.
[Crossref] [Google Scholar]
[19]Deepak S, Ameer PM. Automated categorization of brain tumor from MRI using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing. 2020; 12:5357-69.
[Crossref] [Google Scholar]
[20]Jayaprada S, Jayalakshmi G, Kanyakumari L. Fast hybrid adaboost binary classifier for brain tumor classification. In IOP conference series: materials science and engineering 2021 (pp. 1-9). IOP Publishing.
[Crossref] [Google Scholar]
[21]Yerukalareddy DR, Pavlovskiy E. Brain tumor classification based on MR Images using GAN as a pre-trained model. In ural-siberian conference on computational technologies in cognitive science, genomics and biomedicine 2021 (pp. 380-4). IEEE.
[Crossref] [Google Scholar]
[22]Shafique M, Naseer M, Theocharides T, Kyrkou C, Mutlu O, Orosa L, et al. Robust machine learning systems: challenges, current trends, perspectives, and the road ahead. IEEE Design & Test. 2020; 37(2):30-57.
[Crossref] [Google Scholar]
[23]Nazir M, Shakil S, Khurshid K. Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Computerized Medical Imaging and Graphics. 2021
[Crossref] [Google Scholar]
[24]https://figshare.com/articles/dataset/brain_tumor_dataset/1512427. Accessed 21 July 2021.
[25]https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri. Accessed 21 July 2021.
[26]Manjón JV. MRI preprocessing. In imaging biomarkers 2017 (pp. 53-63). Springer, Cham.
[Crossref] [Google Scholar]
[27]Dhruv B, Mittal N, Modi M. Analysis of different filters for noise reduction in images. In recent developments in control, automation & power engineering 2017 (pp. 410-5). IEEE.
[Crossref] [Google Scholar]
[28]Choi H, Jeong J. Despeckling images using a preprocessing filter and discrete wavelet transform-based noise reduction techniques. IEEE Sensors Journal. 2018; 18(8):3131-9.
[Crossref] [Google Scholar]
[29]Zhang Y, Liu Z, Huang M, Zhu Q, Yang B. Multi-resolution depth image restoration. Machine Vision and Applications. 2021; 32(3):1-5.
[Crossref] [Google Scholar]
[30]Sarkar S, Das S, Chaudhuri SS. Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Applied Soft Computing. 2017; 50:142-57.
[Crossref] [Google Scholar]
[31]Guido RC. A tutorial review on entropy-based handcrafted feature extraction for information fusion. Information Fusion. 2018; 41:161-75.
[Crossref] [Google Scholar]
[32]Srivastava D, Rajitha B, Agarwal S, Singh S. Pattern-based image retrieval using GLCM. Neural Computing and Applications. 2018; 32:10819-32.
[Crossref] [Google Scholar]
[33]Zhou W, Yu L, Qiu W, Zhou Y, Wu M. Local gradient patterns (LGP): an effective local-statistical-feature extraction scheme for no-reference image quality assessment. Information Sciences. 2017; 397:1-14.
[Crossref] [Google Scholar]
[34]Vlachas PR, Pathak J, Hunt BR, Sapsis TP, Girvan M, Ott E, et al. Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Networks. 2020; 126:191-217.
[Crossref] [Google Scholar]
[35]Abraham B, Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybernetics and Biomedical Engineering. 2020; 40(4):1436-45.
[Crossref] [Google Scholar]
[36]Zhu D, Cai C, Yang T, Zhou X. A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing. 2018; 2(1):1-15.
[Crossref] [Google Scholar]
[37]Sarwinda D, Paradisa RH, Bustamam A, Anggia P. Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science. 2021; 179:423-31.
[Crossref] [Google Scholar]
[38]Al-haija QA, Adebanjo A. Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In international IOT, electronics and mechatronics conference 2020 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[39]Vejjanugraha P, Kotani K, Kongprawechnon W, Kondo T, Tungpimolrut K. Automatic screening of lung diseases by 3D active contour method for inhomogeneous motion estimation in CT image pairs. Walailak Journal of Science and Technology. 2021; 18(12).
[Crossref] [Google Scholar]
[40]Phaye SS, Sikka A, Dhall A, Bathula D. Dense and diverse capsule networks: making the capsules learn better. arXiv preprint arXiv:1805.04001. 2018.
[Google Scholar]
[41]Anaraki AK, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics and Biomedical Engineering. 2019; 39(1):63-74.
[Crossref] [Google Scholar]
[42]Tahir B, Iqbal S, Usman GKM, Saba T, Mehmood Z, Anjum A, et al. Feature enhancement framework for brain tumor segmentation and classification. Microscopy Research and Technique. 2019; 82(6):803-11.
[Crossref] [Google Scholar]
[43]Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomedical Signal Processing and Control. 2020.
[Crossref] [Google Scholar]