Gaussian Weighted Deep CNN with LSTM for Brain Tumor Detection

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 1

Year of Publication : 2023

Authors : V. Vinay Kumar, P. Grace Kanmani Prince

10.14445/23488379/IJEEE-V10I1P119

How to Cite?

V. Vinay Kumar, P. Grace Kanmani Prince, "Gaussian Weighted Deep CNN with LSTM for Brain Tumor Detection," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 1, pp. 197-208, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I1P119

Abstract:

Brain tumor is contemplated as a cruel illness in which the accuracy of images has a vital task. Accurate identification of tumor aid in exactly finding the injured portion and so decrease the mortality rate. Given that, learning the hidden pattern is significant to get an enhanced diagnosis and image quality. But, acquiring accurate diagnosis considering different lesion cases is a key concern. To overcome existing works problem, the automatic detection of brain tumor patients from their tumor images, Gaussian Weighted Deep Convolutional Neural Network with LSTM (GWDeepCNN-LSTM), is introduced. GWDeepCNN-LSTM technique includes many layers. First, brain MR images are collected from the given database. The image preprocessing is performed using a Gaussian weighted non-local mean filter where the noisy pixels are eliminated. Then, the segmentation is employed Hartigan's segmentation method to partition the image into similar regions. Followed by feature extraction is performed to extract the more informative features, such as texture, color and intensity, from the segmented image. Later, the classification of brain MR images is executed via Long short-term memory (LSTM). From that, the input image is classified as normal or tumor with higher accuracy. GWDeepCNN-LSTM performs better with an accuracy of disease detection and minimal time and error rate.

Keywords:

Brain tumor, Convolutional Neural Network (CNN), LSTM.

References:

[1] Fatemh Bashir-Gonbadi, and Hassan Khotanlou, “Brain Tumor Classification Using Deep Convolutional Autoencoder-Based Neural Network: Multi-Task Approach,” Multimedia Tools and Applications, vol. 80, pp. 19909–19929, 2021, Crossref, https://doi.org/10.1007/S11042-021-10637-1
[2] Sharan Kumar, and Dattatreya P.Mankame, “Optimization Driven Deep Convolution Neural Network for Brain Tumor Classification,” Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1190-1204, 2020. Crossref, https://doi.org/10.1016/J.Bbe.2020.05.009
[3] Ming Li et al, “Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network,” IEEE Access, vol. 7, pp. 180134–180146, 2019. Crossref, https://doi.org/10.1109/ACCESS.2019.2958370
[4] Weiguang Wang et al., “Learning Methods of Convolutional Neural Network Combined with Image Feature Extraction in Brain Tumor Detection,” IEEE Access, vol. 8, pp. 152659–152668, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.3016282
[5] R. Tamilaruvi et al., "Brain Tumor Detection in MRI Images Using Convolutional Neural Network Technique," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 198-208, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P118
[6] R.Hashemzehi et al., “Detection of Brain Tumors From MRI Images Base on Deep Learning Using Hybrid Model CNN and NADE,” Biocybernetics and Biomedical Engineering,, vol. 40, no. 3, pp. 1225-1232, Crossref, https://doi.org/10.1016/J.Bbe.2020.06.001
[7] Mohamed A.Naser, and M. Jamal Deen, “Brain Tumor Segmentation and Grading of Lower Grade Glioma Using Deep Learning in MRI Images,” Computers in Biology and Medicine, vol. 121, pp. 1-18, 2020, Crossref, https://doi.org/10.1016/J.Compbiomed.2020.103758
[8] Mesut Togacar, Zafer Comert, and Burhan Ergen, “Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method,” Expert Systems with Applications, vol. 149, pp. 1-20, 2020. Crossref, https://doi.org/10.1016/J.Eswa.2020.113274
[9] C. Jaspin Jeba Sheela, and G. Suganthi, “Automatic Brain Tumor Segmentation From MRI Using Greedy Snake Modeland Fuzzy CMeans Optimization,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 3, pp. 1-25, 2019. Crossref, https://doi.org/10.1016/J.Jksuci.2019.04.006
[10] FatihÖzyurt et al., “Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy,” Measurement, pp.1-16, 2019. Crossref, https://doi.org/10.1016/J.Measurement.2019.07.058
[11] G. Karayegen, and Mehmet Feyzi Aksahin, “Brain Tumor Prediction on MR Images with Semantic Segmentation by Using Deep Learning Network and 3D Imaging of Tumor Region,” Biomedical Signal Processing and Control vol. 66, pp.1-14, 2021. Crossref, https://doi.org/10.1016/J.Bspc.2021.102458
[12] M.O. Khairandish et al., “A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images,” IRBM, pp.1-10, 2021, Crossref, https://doi.org/10.1016/J.Irbm.2021.06.003
[13] R. Lokesh Kumar et al., “Multi-Class Brain Tumor Classification Using Residual Network and Global Average Pooling,” Multimedia Tools and Applications, vol. 80, pp. 13429-13438, 2021. Crossref, https://doi.org/10.1007/s11042-020-10335-4
[14] Sindhia et al., "Brain Tumor Detection Using MRI By Classification and Segmentation," SSRG International Journal of Medical Science, vol. 6, no. 3, pp. 12-14, 2019. Crossref, https://doi.org/10.14445/23939117/IJMS-V6I3P103
[15] P. Ramya, M.S. Thanabal, and C.Dharmaraja, “Brain Tumor Segmentation Using Cluster Ensemble and Deep Super Learner for Classification of MRI,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 9939–9952, 2021 Crossref, https://doi.org/10.1007/S12652-021-03390-8
[16] Niloufar Alipour, and Reza P.R. Hasanzadeh, “Superpixel-Based Brain Tumor Segmentation in MR Images Using an Extended Local Fuzzy Active Contour Model,” Multimedia Tools and Applications, vol. 80, pp. 8835–8859, 2020, Crossref, https://doi.org/10.1007/S11042-020-10122-1
[17] Rahul Singh, Aditya Goel, and D.K Raghuvanshi, “M.R. Brain Tumor Classification Employing ICA and Kernel-Based Support Vector Machine,” Signal, Image and Video Processing., vol.15, pp. 501-510, 2021, Crossref, https://doi.org/10.1007/S11760-020-01770-9
[18] Sidra Sajid, Saddam Hussain, and Amna Sarwar, “Brain Tumor Detection and Segmentation in MR Images Using Deep Learning,” Arabian Journal for Science and Engineering, vol. 44, pp. 9249-9261, 2019, Crossref, https://doi.org/10.1007/S13369-019-03967-8
[19] A.S.M Shafi et al., “Classification of Brain Tumors and Auto-Immune Disease Using Ensemble Learning,” Informatics in Medicine Unlocked, vol. 21, pp. 1-8, 2021, Crossref, https://doi.org/10.1016/J.Imu.2021.100608
[20] A. Srinivasa Reddy, and P.Chenna Reddy, “MRI Brain Tumor Segmentation and Prediction Using Modified Region Growing and Adaptive SVM,” Soft Computing, vol. 25, pp. 4135–4148, 2021, Crossref, https://doi.org/10.1007/s00500-020-05493-4
[21] G. R. Meghana, Suresh Kumar Rudrahithlu, and K. C. Shilpa, "Detection of Brain Cancer Using Machine Learning Techniques a Review," SSRG International Journal of Computer Science and Engineering, vol. 9, no. 9, pp. 12-18, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I9P102
[22] Mohammadreza Soltaninejad et al., “Automated Brain Tumour Detection and Segmentation Using Superpixel-Based Extremely Randomized Trees in FLAIR MRI,” International Journal of Computer Assisted Radiology and Surgery, vol. 12, pp. 183–203, 2017, Crossref, https://doi.org/10.1007/S11548-016-1483-3
[23] Rajeev Kumar Gupta et al., “Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks,” Interdisciplinary Sciences, Computational Life Sciences, vol. 14, no. 2, pp. 485-502, 2022, Crossref, https://doi.org/10.1007/S12539- 022-00502-6
[24] N. Varuna Shree, and T. N. R. Kumar, “Identification and Classification of Brain Tumor MRI Images with Feature Extraction Using DWT and Probabilistic Neural Network,” Brain Informatics, vol. 5, pp. 23–30, 2018, Crossref, https://doi.org/10.1007/s40708-017- 0075-5
[25] P. Harish, and S. Baskar, “MRI Based Detection and Classification of Brain Tumor Using Enhanced Faster R-CNN and Alex Net Model,” Materials Today: Proceedings, pp. 1-15, 2020, Crossref, https://doi.org/10.1016/j.matpr.2020.11.495
[26] Halima El Hamdaoui et al., “High Precision Brain Tumor Classification Model Based on Deep Transfer Learning and Stacking Concepts,” The Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 167-177, 2021, Crossref, http://doi.org/10.11591/ijeecs.v24.i1.pp167-177
[27] Ibrahima Sory Keita et al., "Classification of Benign and Malignant Mris Using SVM Classifier for Brain Tumor Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 234-240, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P226
[28] R. Usha, and K. Perumal, “A Modified Fractal Texture Image Analysis Based on Grayscale Morphology for Multi-Model Views in MR Brain,” The Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 1, pp. 154-163, Crossref, http://doi.org/10.11591/ijeecs.v21.i1.pp154-163
[29] Zahraa Yaseen Hasan, “Fusion for Medical Image Based on Discrete Wavelet Transform Coefficient,” The Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 3, pp. 1407-1416, Crossref, http://doi.org/10.11591/ijeecs.v21.i3.pp1407- 1416
[30] P. Seetha Subha Priya et al., "A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis," International Journal of Engineering Trends and Technology, vol. 68, no. 10, pp. 63-66, 2020. Crossref, http://doi.org/10.14445/22315381/IJETT-V68I10P211
[31] Sarah Ali Abdelaziz Ismael, Ammar Mohammed, and Hesham Hefny, “An Enhanced Deep Learning Approach for Brain Cancer MRI Images Classification Using Residual Networks,” Artificial Intelligence in Medicine, vol. 102, pp. 1-15, 2020. Crossref, https://doi.org/10.1016/J.Artmed.2019.101779
[32] Mohammad Shahjahan Majib et al., “VGG-Scnet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images,” IEEE Access, vol. 9, pp. 116942 – 116952, 2021, Crossref, https://doi.org/10.1109/ACCESS.2021.3105874
[33] Fareed M.Mohammed, Mustafa M.Essa, and Ahmed W. Maseer, "Comparison Between MRI and CT-Scan in Diagnosis the Brain Tumor Images," SSRG International Journal of Medical Science, vol. 6, no. 5, pp. 1-4, 2019. Crossref, https://doi.org/10.14445/23939117/IJMS-V6I5P101
[34] Javaria Amin et al., “Brain Tumor Detection By Using Stacked Autoencoders in Deep Learning,” Journal of Medical Systems, vol. 44, no. 32, pp. 1-19, 2020, Crossref, https://doi.org/10.1007/s10916-019-1483-2
[35] Md. Lizur Rahman, Ahmed Wasif Reza, and Shaiful Islam Shabuj “An Internet of Things-Based Automatic Brain Tumor Detection System,” The Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 1, pp. 214-222, Crossref, http://doi.org/10.11591/ijeecs.v25.i1.pp214-222
[36] Simon Tongbram, Benjamin A. Shimray, and Loitongbam Surajkumar Singh, “Segmentation of Image Based on K-Means and Modified Subtractive Clustering,” The Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 3, pp. 1396-1403, Crossref, http://doi.org/10.11591/ijeecs.v22.i3.pp1396-1403