Deep Learning Approach to Detect Gliomas Brain Tumour through Classification at Earlier Stage
Keywords:
Fast Recurrent Convolutional Neural Networks, Gliomas, Brain Tumour, Classification; Deep LearningAbstract
Brain tumors with glial cell origins are known as Gliomas. Treatment and Prognosis planning depends greatly on how these tumors are graded and categorized. World Health Organization (WHO) introduced the Central Nervous System (CNS) for Gliomas classification standards. Genomics and Histology combined to meet the categorization criteria for Gliomas. The molecular approaches to CNS Tumour Taxonomy Consortium were founded in 2017 and offer current guidelines for classifying CNS tumours. In this paper, proposed the novel technique in suggesting a new Gliomas analytical method which integrates digital analysis-derived cellularity features in the incorporation of molecular characteristics into images of brain histopathology. A novel over-segmentation approach proposed to identify Region of Interest (ROI) in large histopathology datasets, an enhanced tumour classification method based on the fusion of cellularity and molecular features is then developed using Fast Recurrent Convolutional Neural Networks (FRCNN). Using data from the Cancer Genome Atlas, evaluate the proposed method using 549 patient cases. For Higher and Lower-grade Gliomas which are HGG and LGG with the FRCNN achieved 93.81% cross-validation accuracy LGG II and LGG III both achieved 98.6%
Downloads
References
. Singh, G., Manjila, S., Sakla, N., True, A., Wardeh, A. H., Beig, N., ... &Spektor, V. (2021). Radiomics and radiogenomics in Gliomas: a contemporary update. British Journal of Cancer, 125(5), 641-657.
. Abdel Razek, A. A. K., Alksas, A., Shehata, M., AbdelKhalek, A., Abdel Baky, K., El-Baz, A., & Helmy, E. (2021). Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights into Imaging, 12(1), 1-17.
. Yan, J., Zhang, B., Zhang, S., Cheng, J., Liu, X., Wang, W., ... & Zhang, Z. (2021). Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precision Oncology, 5(1), 1-9.
. Davendralingam, N., Sebire, N. J., Arthurs, O. J., & Shelmerdine, S. C. (2021). Artificial intelligence in paediatric radiology: future opportunities. The British Journal of Radiology, 94(1117), 20200975.
. Overcast, W. B., Davis, K. M., Ho, C. Y., Hutchins, G. D., Green, M. A., Graner, B. D., & Veronesi, M. C. (2021). Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors. Current Oncology Reports, 23(3), 1-15.
. Ouerghi, H., Mourali, O., &Zagrouba, E. (2022). Glioma classification via mr images radiomics analysis. The Visual Computer, 38(4), 1427-1441.
. Marappan, R., Vardhini, P. H., Kaur, G., Murugesan, S., Kathiravan, M., Bharathiraja, N., & Venkatesan, R. (2023). Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks. Soft Computing, 27(16), 11853-11867.
. Severn, C., Suresh, K., Görg, C., Choi, Y. S., Jain, R., & Ghosh, D. (2022). A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features. Sensors, 22(14), 5205.
. Gore, S., & Jagtap, J. (2022). Radiogenomic analysis: 1p/19q codeletion based subtyping of low-grade glioma by analysing advanced biomedical texture descriptors. Journal of King Saud University-Computer and Information Sciences, 34(10), 8449-8458.
. Bhatele, K. R., &Bhadauria, S. S. (2022). Machine learning application in Glioma classification: review and comparison analysis. Archives of Computational Methods in Engineering, 29(1), 247-274.
. Wang, D., Liu, C., Wang, X., Liu, X., Lan, C., Zhao, P., ... & Liu, Y. (2021). Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma. Frontiers in Bioinformatics, 1, 718697.
. Zhou, Q., Ke, X., Xue, C., Li, S., Huang, X., Zhang, B., & Zhou, J. (2022). A Nomogram for Predicting Early Recurrence in Patients with High-Grade Gliomas. World Neurosurgery, 164, e619-e628.
. Alekhya, B., Sasikumar, R., Kumar, N. S., & Bharathiraja, N. (2023). Hybrid ICHO-HSDC Model For Accurate Covid-19 Detection and Classification From CT Scan And X-Ray Images. International Journal Of Computers Communications & Control, 18(4).
. Ong, W., Zhu, L., Zhang, W., Kuah, T., Lim, D. S. W., Low, X. Z., ... & Hallinan, J. T. P. D. (2022). Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers, 14(16), 4025.
. Jayanthi, E., Ramesh, T., Kharat, R. S., Veeramanickam, M. R. M., Bharathiraja, N., Venkatesan, R., & Marappan, R. (2023). Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies. Soft Computing, 27(11), 7555-7565.
. Kalasauskas, D., Kosterhon, M., Keric, N., Korczynski, O., Kronfeld, A., Ringel, F., ... &Brockmann, M. A. (2022). Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers, 14(3), 836.
. Gore, S., & Jagtap, J. (2021). MRI based genomic analysis of glioma using three pathway deep convolutionalneural network for IDH classification. Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), 2728-2741.
. Ravindhar, N. V., Sasikumar, S., & Bharathiraja, N. (2024). Integration of cloud-based scheme with industrial wireless sensor network for data publishing in privacy of point source. International Journal of Cloud Computing, 13(2), 124-138.
. Cheung, H. M. C., & Rubin, D. (2021). Challenges and opportunities for artificial intelligence in oncological imaging. Clinical Radiology, 76(10), 728-736.
. Admoni-Elisha, L., Elbaz, T., Chopra, A., Shapira, G., Bedford, M. T., Fry, C. J., ... & Levy, D. (2022). TWIST1 methylation by SETD6 selectively antagonizes LINC-PINT expression in glioma. Nucleic acids
. Chaddad, A., Katib, Y., & Hassan, L. (2021). Future artificial intelligence tools and perspectives in medicine. Current Opinion in Urology, 31(4), 371-377.
. Sulaiman, A., Nagu, B., Kaur, G., Karuppaiah, P., Alshahrani, H., Reshan, M. S. A., ... & Shaikh, A. (2023). Artificial Intelligence-Based Secured Power Grid Protocol for Smart City. Sensors, 23(19), 8016.
. Lam, L. H. T., Do, D. T., Diep, D. T. N., Nguyet, D. L. N., Truong, Q. D., Tri, T. T., ... & Le, N. Q. K. (2022). Molecular subtype classification of low‐grade Gliomas using magnetic resonance imaging‐based radiomics and machine learning. NMR in Biomedicine, 35(11), e4792.
. Talari, P., N, B., Kaur, G., Alshahrani, H., Al Reshan, M. S., Sulaiman, A., & Shaikh, A. (2024). Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2. Plos one, 19(1), e0292100.
. Rani, R. M., Dwarakanath, B., Kathiravan, M., Murugesan, S., Bharathiraja, N., & Vinoth Kumar, M. (2024). Accurate artificial intelligence method for abnormality detection of CT liver images. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16.
. Nagu, B., Arjunan, T., Bangare, M. L., Karuppaiah, P., Kaur, G., & Bhatt, M. W. (2023). Ultra-low latency communication technology for Augmented Reality application in mobile periphery computing. Paladyn, Journal of Behavioral Robotics, 14(1), 20220112.
. Bharathiraja, N., Pradeepa, K., Sheela, I. J., Sudhakar, G., Kumar, M. V., & Kaur, G. (2023, January). Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1723-1727). IEEE.
. Rajini, S. N. S., Veeramanickam, M. R. M., Anuradha, K., Marappan, R., Kirubadevi, T., Bharathiraja, N., ... & Bhaskaran, S. (2023, January). Soldier’s Position Tracking & Health Monitoring Optimization Model Using Biosensors. In 2023 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-4). IEEE.
. Pradeepa, K., Bharathiraja, N., Meenakshi, D., Hariharan, S., Kathiravan, M., & Kumar, V. (2022, December). Artificial Neural Networks in Healthcare for Augmented Reality. In 2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) (pp. 1-5). IEEE.
. Bhaskaran, S., Veeramanickam, M. R. M., Hariharan, S., Bharathiraja, N., Pradeepa, K., & Marappan, R. (2022, December). Sentiment Analysis Model using Text and Emoticons for Pharmaceutical & Healthcare Industries. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-4). IEEE.
. Challa, N. P., Shanmuganathan, C., Shobana, M., Deepthi, C. V. S., & Bharathiraja, N. (2023). Deep learning based on multimedia encoding to enhance video quality. International Journal of Communication Networks and Distributed Systems, 29(5), 475-492.
. Bharathiraja, N., Shobana, M., Anand, M. V., Lathamanju, R., Shanmuganathan, C., & Arulkumar, V. (2023). A secure and effective diffused framework for intelligent routing in transportation systems. International Journal of Computer Applications in Technology, 71(4), 363-370.
. Pandithurai, O., Urmela, S., Murugesan, S., & Bharathiraja, N. (2023). A secured industrial wireless iot sensor network enabled quick transmission of data with a prototype study. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16.
. Ravindhar, N., Sasikumar, S., Bharathiraja, N., & Kumar, M. V. (2022). Secure integration of wireless sensor network with cloud using coded probable bluefish cryptosystem. J. Theor. Appl. Inf. Technol, 100, 7438-7449.
. Bharathiraja, N., Pradeepa, K., Murugesan, S., Hariharan, S., & Veeramanickam, M. R. M. (2022, December). A Novel Framework for Cyber Security Attacks on Cloud-Based Services. In 2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) (pp. 1-4). IEEE.
. Bhaskaran, S., Hariharan, S., Veeramanickam, M. R., Bharathiraja, N., Pradeepa, K., & Marappan, R. (2022, December). Recommendation system using inference-based graph learning–modeling and analysis. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-5). IEEE.
. Anand, M., Antonidoss, A., Balamanigandan, R., Rahmath Nisha, S., Gurunathan, K., & Bharathiraja, N. (2022). Resourceful Routing Algorithm for Mobile Ad-Hoc Network to Enhance Energy Utilization. Wireless Personal Communications, 127(Suppl 1), 7-8.
. Menaka, S., Harshika, J., Philip, S., John, R., Bharathiraja, N., & Murugesan, S. (2023, February). Analysing the accuracy of detecting phishing websites using ensemble methods in machine learning. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1251-1256). IEEE.
. Murugesan, S., Bharathiraja, N., Pradeepa, K., Ravindhar, N. V., Kumar, M. V., & Marappan, R. (2023, March). Applying machine learning & knowledge discovery to intelligent agent-based recommendation for online learning systems. In 2023 International Conference on Device Intelligence, Computing and Communication Technologies,(DICCT) (pp. 321-325). IEEE.
. Pandithurai, O., Bharathiraja, N., Pradeepa, K., Meenakshi, D., & Kathiravan, M. (2023, February). Air Pollution Prediction using Supervised Machine Learning Technique. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 542-546). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
