Abstract
An unusual increase of nerves inside the brain, which disturbs the actual working of the brain, is called a brain tumor. It has led to the death of lots of lives. To save people from this disease timely detection and the right cure is the need of time. Finding tumor-affected cells in the human brain is a cumbersome and time- consuming task. However, the accuracy and time required to detect brain tumors is a big challenge in the arena of image processing. This research paper proposes an innovative, accurate and optimized system to detect brain tumors. The system follows the activities like, preprocessing, segmentation, feature extraction, optimization and detection. The preprocessing system uses a compound filter, which is a composition of Gaussian, mean and median filters. Threshold and histogram techniques are applied for image segmentation. Grey level co- occurrence matrix is used for feature extraction. The optimized convolution neural network (CNN) technique is applied here that uses ant colony optimization, bee colony optimization and particle swarm optimization, genetic algorithm, gray wolf optimization and whale optimization algorithm techniques for best feature selection. Detection of brain tumors is achieved through CNN classifiers. This system compares its performance with another modern technique of optimization by using accuracy, precision and recall parameters and claims the supremacy of this work. This system is implemented in the Python programming language. The brain tumor detection accuracy of this optimized system has been measured at 98.9%.
Similar content being viewed by others
References
Abdel-Gawad AH, Lobna A, Ahmed S, Radwan G (2020) Optimized edge detection technique for brain tumor detection in MR images. IEEE Access 8:136243–136259
Aly RHM, Rahouma KH, Hamed HFA (2019) Brain tumors diagnosis and prediction based on applying the learning metaheuristic optimization techniques of particle swarm, ant colony and bee colony. In: 16th LT learning and technology conference 2019. Procedia Computer Science 163: 165–179
Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imag 2017, Article ID 9749108, 12 pages. https://doi.org/10.1155/2017/9749108
Bhima K, Jagan A (2016) Analysis of MRI based brain tumoridentification using segmentation technique. Int Conf Commun Signal Process (ICCSP) 2016:2109–2113
Borole VY et al (2015) Image processing techniques for brain tumor detection: a review. Int J Emerg Trends Technol Comput Sci 4:28–32
Geetha A, Gomathi N (2019) A robust grey wolf-based deep learning for brain tumour detection in MR images. Biomed Eng-Biomed Tech 65(2):191–207. https://doi.org/10.1515/bmt-2018-0244
Hebli AP, Gupta S (2016) Brain tumor detection using image processing: a survey. In: Proceedings of 65th IRF international conference
Hossain T, Fairuz SS, Mohsena A, Abdullah AN, Faisal MS (2019) Brain tumor detection using convolutional neural network. In: 1st international conference on advances in science, engineering and robotics technology (ICASERT-2019)
Irmak E (2021) Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Trans Electr Eng 45:1015–1036
Kapoor L, Thakur S (2017) A survey on brain tumor detection using image processing techniques. In: 2017 7th international conference on cloud computing, data science & engineering confluence, 582–585
Kumar P, Vijayakumar B (2016) An efficient brain tumor MRI segmentation and classification using GLCM texture features and feed forward neural networks. World J Med Sci 13:85–92
Medical Imaging in Cancer Care: Charting the Progress, US Oncology and National Electrical Manufacturers Association (2012).
Mishra PK, Satapathy SC, Rout M (2021) Segmentation of MRI brain tumor image using optimization based deep convolutional neural networks (DCNN). Open Comput Sci 11:380–390
Parihar AS (2017) A study on brain tumor segmentation using convolution neural network. In: 2017 international conference on inventive computing and informatics (ICICI)
Rajagopal R (2019) Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features. Int J Imag Syst Technol 29:353–359
Ramamurthy D, Mahesh PK (2022) Whale Harris Hawks optimization based deep learning classifier for brain tumor detection using MRI images. J King Saud Univ-Comput Inf Sci 34(6):3259–3272. https://doi.org/10.1016/j.jksuci.2020.08.006
Ramtekkar PK, Pandey A, Pawar MK (2020) A proposed model for automation of detection and classification of brain tumor by deep learning. In: 2020 2nd international conference on data, engineering and applications (IDEA)
Shankar K, Lakshmanaprabu SK, Khanna A, Tanwar S, Joel J, PC, Rodrigues, NR, Roy (2019) Alzheimer detection using group grey wolf optimization based features with convolutional classifier. Comput Electr Eng 77:230–243
Sharifa M, Amina J, Razaa M, Yasmina M, Satapathy SC (2020) An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recogn Lett 129:150–157
Sindhu A, Radha V (2020) An optimal feature selection with whale algorithm and adaboost ensemble model for pancreatic cancer classification in PET/CT images. Biosci Biotech Res Comm 13(4):1886–1894
Yina B, Wang C, Abzac F (2020) New brain tumor classification method based on an improved version of whale optimization algorithm. Biomed Signal Process Control 56:1–10
Funding
There is no funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Author declares there is no conflict of interest.
Ethics approval
This article contains no studies related to animals.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ramtekkar, P.K., Pandey, A. & Pawar, M.K. Innovative brain tumor detection using optimized deep learning techniques. Int J Syst Assur Eng Manag 14, 459–473 (2023). https://doi.org/10.1007/s13198-022-01819-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-022-01819-7