Detection and Classification of Automated Brain Stroke Lesion with optimized Dual Stage Deep Stacked Auto-Encoder
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Abstract
Purpose: The brain is the main organ of the human body. Stroke, also known as cerebral thrombosis, is a medical condition in which a rupture occurs in the blood vessels in the brain, resulting in brain damage. Symptoms may appear when the brain's blood flow and other nutrients are disrupted. A stroke is a medical emergency that can lead to long-term neurological impairment, resulting in complications and, in some cases, fatalities.
Materials and Methods: According to the World Health Organization, stroke is the primary determinant of mortality and disability globally. The early identification of various cardiovascular warning signs can reduce the impact of a stroke. The brain stroke dataset is used in existing methods. Delimitation of classifying stroke is difficult because the complication of lesion shapes and acquiring a ground truth is problematic, as it requires high clinical expertise and anatomical knowledge. In this manuscript, a Detection and Classification of Automated Brain Stroke Lesion with optimized Dual Stage Deep Stacked Auto-Encoder is proposed to detect brain stroke at an early stage with great accuracy.
Results: The input image is taken from slice-level Non-Contrast CT pictures dataset. The collected images are pre-processed and enhanced by removing skull regions, and then the rotations are performed by midline symmetry process. The preprocessed ROI region is fed to feature extraction, and the features are extracted using the wavelet domain. Next, the extracted features are given to classification and classified using Dual stage deep stacked auto encoder (DS-DSAE) optimized with Evolved Gradient Descent Optimization (EGDO) to effectively classify acute infarct, chronic infarct and ischemic stroke, haemorrhagic stroke, and normal.
Conclusion: The goal is to reduce computing complexity and enhance accuracy. The performance of the proposed wavelet-DS-DSAE-EGDO method achieves High accuracy 30.56%, 12.32%, 15.6%, 16.6%, 25.6%, 32.2%; High Precision 28.74%, 32.2%, 14.5%, 16.55%, 17.8%, 23.4% is comparing with the existing methods.
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5- Amin, J., Sharif, M., Yasmin, M., Saba, T., Anjum, M. A., Fernandes, S. L. “A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. Journal of Medical Systems, 43(11), pp. 1-16, (2019).
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7- Rajesh, P., Shajin, F.H., Kumaran, G.K., “An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization”, Applied Science and Engineering Progress, 15(3), pp.5514-5514, (2022).
8- Shajin, F.H., Rajesh, P., Raja, M.R., “An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC”, Circuits, Systems, and Signal Processing, 41(3), pp.1751-1774, (2022).
9- Mikhail, P., Le, M. G. D., Mair, G. “Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review. Journal of Stroke and Cerebrovascular Diseases, 29(5), p.104715, (2020).
10- Rajesh, P., Shajin, F.H., Kannayeram, G., “A novel intelligent technique for energy management in smart home using internet of things”, Applied Soft Computing, 128, p.109442, (2022).
11- Thiyagarajan, S. K., Murugan, K. “A Systematic Review on Techniques Adapted for Segmentation and Classification of Ischemic Stroke Lesions from Brain MR Images”, Wireless Personal Communications, pp.1-20, (2021).
12- Bharathi, P. G., Agrawal, A., Sundaram, P., Sardesai, S. “Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images”, Biocybernetics and Biomedical Engineering, 39(2), pp.410-425, (2019).
13- Amin, J., Sharif, M., Raza, M., Yasmin, M. “Detection of brain tumor based on features fusion and machine learning”, Journal of Ambient Intelligence and Humanized Computing, pp.1-17, (2018).
14- Cho, J., Park, K. S., Karki, M., Lee, E., Ko, S., Kim, J. K., Park, S. “Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models”, Journal of Digital Imaging, 32(3), pp.450-461, (2019).
15- Kuang, H., Menon, B. K., Qiu, W. “Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up non-contrast CT images in patients with acute ischemic stroke”, IEEE Access, 7, pp.39842-39851.
16- Wolff, L., Berkhemer, O. A., van Es, A. C., van Zwam, W. H., Dippel, D. W., Majoie, C. B., van der Lugt, A. “Validation of automated Alberta Stroke Program Early CT Score (ASPECTS) software for detection of early ischemic changes on non-contrast brain CT scans”, Neuroradiology, 63(4), pp.491-498, (2021).
17- Nishio, M., Koyasu, S., Noguchi, S., Kiguchi, T., Nakatsu, K., Akasaka, T., Itoh, K. “Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model”, Computer Methods and Programs in Biomedicine, 196, p.105711, (2020).
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19- Praveen, G. B., Agrawal, A., Sundaram, P., Sardesai, S. “Ischemic stroke lesion segmentation using stacked sparse autoencoder”, Computers in biology and medicine, 99, pp.38-52, (2018).
20- Pawar, S. S., Prasanth, Y. “Multi-Objective Optimization Model for QoS-Enabled Web Service Selection in Service-Based Systems”, New Review of Information Networking, 22(1), pp.34-53, (2017).
21- Bhargavi, V. R., Rajesh, V. “Computer aided bright lesion classification in fundus image based on feature extraction”, International Journal of Pattern Recognition and Artificial Intelligence, 32(11), p.1850034, (2018).
22- Bhagavan, K., Imambi, S. S., Bano, S. “A compressive survey on different image processing techniques to identify the brain tumor”, International Journal of Engineering & Technology, 7(2.7), pp.1081-1084, (2018).
23- Parvathy, V. S., Pothiraj, S., Sampson, J. Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT”, In Smart Sensors for Industrial Internet of Things (pp. 127-146). Springer, Cham, (2021).
24- Gautam, A., Raman, B. “Towards effective classification of brain hemorrhagic and ischemic stroke using CNN”, Biomedical Signal Processing and Control, 63, p.102178, (2021).
25- Mariano, V., Tobon Vasquez, J.A., Casu, M.R., Vipiana, F. “Brain Stroke Classification via Machine Learning Algorithms Trained with a Linearized Scattering Operator”, Diagnostics, 13(1), p.23, (2022).
26- Vupputuri, A., Ashwal, S., Tsao, B., Ghosh, N. “Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering”, Computers in Biology and Medicine, 116, p.103536, (2020).
27- Akalya devi, C., Karthika Renuka, D. “Multimodal Emotion Recognition Framework Using a Decision-Level Fusion and Feature-Level Fusion Approach”, IETE Journal of Research, pp.1-12, (2023).
28- Bangare, S. L., Pradeepini, G., Patil, S. T. “Brain tumor classification using mixed method approach. In 2017 International Conference on Information Communication and Embedded Systems (ICICES) (pp. 1-4). IEEE.
29- Yi, D., Ji, S., Bu, S. “An enhanced optimization scheme based on gradient descent methods for machine learning”, Symmetry, 11(7), pp.942, (2021).
30- Rajesh, K. D., Krishna, M. M., Ali, M. A., Chalapathi, P. V. A modified hybrid similarity coefficient based method for solving the cell formation problem in cellular manufacturing system”, Materials Today: Proceedings, 4(2), pp.1469-1477, (2017).
31- Sasikala, E., Kanmani, P., Gopalakrishnan, R., Radha, R. “Identification of lesion using an efficient hybrid algorithm for MRI brain image segmentation”, Journal of Ambient Intelligence and Humanized Computing, pp.1-11, (2021).
32- Wu, G., Chen, X., Lin, J., Wang, Y., Yu, J. “Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model. Medical Physics, 48(3), pp.1262-1275, (2021).
33- Lo, C. M., Hung, P. H., Lin, D. T. “Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks”, Journal of Digital Imaging, pp.1-10, (2021).
34- Subudhi, A., Dash, M., Sabut, S. “Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier”, Biocybernetics and Biomedical Engineering, 40(1), pp.277-289, (2020).
35- Subudhi, A., Dash, P., Mohapatra, M., Tan, R.S., Acharya, U.R., Sabut, S. “Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review”, Diagnostics, 12(10), p.2535, (2022).
36- Deepa, B., Murugappan, M., Sumithra, M.G., Mahmud, M., Al-Rakhami, M.S. “Pattern descriptors orientation and map firefly algorithm based brain pathology classification using hybridized machine learning algorithm”, IEEE Access, 10, pp.3848-3863, (2021).
2- Abdallah, Y. “Segmentation of Brain Stroke Lesions using Marker-based Algorithms in CT images”, In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-4). IEEE, (2020).
3- Sarmento, R. M., Vasconcelos, F. F., RebouçasFilho, P. P., de Albuquerque, V. H. C. “An IoT platform for the analysis of brain CT images based on Parzen analysis”, Future Generation Computer Systems, 105, pp.135-147, (2020).
4- Clerigues, A., Valverde, S., Bernal, J., Freixenet, J., Oliver, A., Lladó, X. “Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks”, Computers in biology and medicine, 115, p.103487, (2019).
5- Amin, J., Sharif, M., Yasmin, M., Saba, T., Anjum, M. A., Fernandes, S. L. “A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning. Journal of Medical Systems, 43(11), pp. 1-16, (2019).
6- Shajin, F.H., Rajesh, P., Nagoji Rao, V.K., “Efficient Framework for Brain Tumour Classification using Hierarchical Deep Learning Neural Network Classifier”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-8, (2022).
7- Rajesh, P., Shajin, F.H., Kumaran, G.K., “An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization”, Applied Science and Engineering Progress, 15(3), pp.5514-5514, (2022).
8- Shajin, F.H., Rajesh, P., Raja, M.R., “An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC”, Circuits, Systems, and Signal Processing, 41(3), pp.1751-1774, (2022).
9- Mikhail, P., Le, M. G. D., Mair, G. “Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review. Journal of Stroke and Cerebrovascular Diseases, 29(5), p.104715, (2020).
10- Rajesh, P., Shajin, F.H., Kannayeram, G., “A novel intelligent technique for energy management in smart home using internet of things”, Applied Soft Computing, 128, p.109442, (2022).
11- Thiyagarajan, S. K., Murugan, K. “A Systematic Review on Techniques Adapted for Segmentation and Classification of Ischemic Stroke Lesions from Brain MR Images”, Wireless Personal Communications, pp.1-20, (2021).
12- Bharathi, P. G., Agrawal, A., Sundaram, P., Sardesai, S. “Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images”, Biocybernetics and Biomedical Engineering, 39(2), pp.410-425, (2019).
13- Amin, J., Sharif, M., Raza, M., Yasmin, M. “Detection of brain tumor based on features fusion and machine learning”, Journal of Ambient Intelligence and Humanized Computing, pp.1-17, (2018).
14- Cho, J., Park, K. S., Karki, M., Lee, E., Ko, S., Kim, J. K., Park, S. “Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models”, Journal of Digital Imaging, 32(3), pp.450-461, (2019).
15- Kuang, H., Menon, B. K., Qiu, W. “Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up non-contrast CT images in patients with acute ischemic stroke”, IEEE Access, 7, pp.39842-39851.
16- Wolff, L., Berkhemer, O. A., van Es, A. C., van Zwam, W. H., Dippel, D. W., Majoie, C. B., van der Lugt, A. “Validation of automated Alberta Stroke Program Early CT Score (ASPECTS) software for detection of early ischemic changes on non-contrast brain CT scans”, Neuroradiology, 63(4), pp.491-498, (2021).
17- Nishio, M., Koyasu, S., Noguchi, S., Kiguchi, T., Nakatsu, K., Akasaka, T., Itoh, K. “Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model”, Computer Methods and Programs in Biomedicine, 196, p.105711, (2020).
18- Jyothula, H., Rao, S. K., Vallikumari, V. “Two-phase active counter mechanism embedded with particle swarm optimization technique for segmentation of biomedical images”, Journal of Advanced Research in Dynamical and Control Systems, 9(6), pp.232-242, (2017).
19- Praveen, G. B., Agrawal, A., Sundaram, P., Sardesai, S. “Ischemic stroke lesion segmentation using stacked sparse autoencoder”, Computers in biology and medicine, 99, pp.38-52, (2018).
20- Pawar, S. S., Prasanth, Y. “Multi-Objective Optimization Model for QoS-Enabled Web Service Selection in Service-Based Systems”, New Review of Information Networking, 22(1), pp.34-53, (2017).
21- Bhargavi, V. R., Rajesh, V. “Computer aided bright lesion classification in fundus image based on feature extraction”, International Journal of Pattern Recognition and Artificial Intelligence, 32(11), p.1850034, (2018).
22- Bhagavan, K., Imambi, S. S., Bano, S. “A compressive survey on different image processing techniques to identify the brain tumor”, International Journal of Engineering & Technology, 7(2.7), pp.1081-1084, (2018).
23- Parvathy, V. S., Pothiraj, S., Sampson, J. Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT”, In Smart Sensors for Industrial Internet of Things (pp. 127-146). Springer, Cham, (2021).
24- Gautam, A., Raman, B. “Towards effective classification of brain hemorrhagic and ischemic stroke using CNN”, Biomedical Signal Processing and Control, 63, p.102178, (2021).
25- Mariano, V., Tobon Vasquez, J.A., Casu, M.R., Vipiana, F. “Brain Stroke Classification via Machine Learning Algorithms Trained with a Linearized Scattering Operator”, Diagnostics, 13(1), p.23, (2022).
26- Vupputuri, A., Ashwal, S., Tsao, B., Ghosh, N. “Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering”, Computers in Biology and Medicine, 116, p.103536, (2020).
27- Akalya devi, C., Karthika Renuka, D. “Multimodal Emotion Recognition Framework Using a Decision-Level Fusion and Feature-Level Fusion Approach”, IETE Journal of Research, pp.1-12, (2023).
28- Bangare, S. L., Pradeepini, G., Patil, S. T. “Brain tumor classification using mixed method approach. In 2017 International Conference on Information Communication and Embedded Systems (ICICES) (pp. 1-4). IEEE.
29- Yi, D., Ji, S., Bu, S. “An enhanced optimization scheme based on gradient descent methods for machine learning”, Symmetry, 11(7), pp.942, (2021).
30- Rajesh, K. D., Krishna, M. M., Ali, M. A., Chalapathi, P. V. A modified hybrid similarity coefficient based method for solving the cell formation problem in cellular manufacturing system”, Materials Today: Proceedings, 4(2), pp.1469-1477, (2017).
31- Sasikala, E., Kanmani, P., Gopalakrishnan, R., Radha, R. “Identification of lesion using an efficient hybrid algorithm for MRI brain image segmentation”, Journal of Ambient Intelligence and Humanized Computing, pp.1-11, (2021).
32- Wu, G., Chen, X., Lin, J., Wang, Y., Yu, J. “Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model. Medical Physics, 48(3), pp.1262-1275, (2021).
33- Lo, C. M., Hung, P. H., Lin, D. T. “Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks”, Journal of Digital Imaging, pp.1-10, (2021).
34- Subudhi, A., Dash, M., Sabut, S. “Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier”, Biocybernetics and Biomedical Engineering, 40(1), pp.277-289, (2020).
35- Subudhi, A., Dash, P., Mohapatra, M., Tan, R.S., Acharya, U.R., Sabut, S. “Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review”, Diagnostics, 12(10), p.2535, (2022).
36- Deepa, B., Murugappan, M., Sumithra, M.G., Mahmud, M., Al-Rakhami, M.S. “Pattern descriptors orientation and map firefly algorithm based brain pathology classification using hybridized machine learning algorithm”, IEEE Access, 10, pp.3848-3863, (2021).
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| Issue | Vol 13 No 1 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20782 | |
| Keywords | ||
| Dual Stage Deep Stacked Auto-Encoder Evolved Gradient Descent Optimization scheme Wavelet Domain Non-Contrast Computed Tomography Images Brain Stroke Lesions | ||
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How to Cite
1.
Melingi SB, Tamizhselvan C, Surender R, Reddy VK, Mojjada RK. Detection and Classification of Automated Brain Stroke Lesion with optimized Dual Stage Deep Stacked Auto-Encoder. Frontiers Biomed Technol. 2026;13(1):148-163.
