Optimized CNN-based Brain Tumor Segmentation and Classification using Artificial Bee Colony and Thresholding


  • P. Ashok Babu Institute of Aeronautical Enginnering, Hyderbad, India
  • B.V. Subba Rao Department of Information Technology, PVP Siddhartha Institute of Technology, Andhra Pradesh, India
  • Y. Vijay Bhaskar Reddy Lakireddy Bali Reddy College of Engineering, India
  • G . Rajendra Kumar Vignan’s Institute of Information Technology, India
  • J. Nageswara Rao Lakireddy Bali Reddy College of Engineering, Mylavaram, India
  • Surendra Kumar Reddy Koduru Business Intelligence & Reporting Lead, Newburgh, Indiana, United States
  • G. Sunil Kumar CMR College of Engineering & Technology, Hyderabad, India




Artificial Bee Colony , Brain tumor, Magnetic Resonance Image, Medical Image Analysis, Thresholding method


One of the most important tasks used by the medical profession for disease identification and recovery preparation is automatic medical image processing. Statistical approaches are the most commonly used algorithms, and they consist several important step. Brain tumors are the foremost causes of death of cancerous diseases all over the world. The hippocampus is the human body’s primary control structure. Since a tumor attacks the brain, it can kill the patient if it is not detected early. Among the various imaging modalities available, Magnetic Resonance Image (MRI) is a better implement for calculating area and classifying tumors based on their grade. MRI does not emit any toxic radiation. There is currently no automated method for detecting and identifying the grade of a tumor. This study mainly focusses on classifying and segmenting brain tumors from MRI scan data. It aids physicians in the planning of future care or surgery. This procedure consists of four steps: image de-noising, tumor extraction, attribute extraction, and hybrid classification. In the first step of image de-noising, the curvelet transformation (CT) is used. Then, in the next stage, Artificial Bee Colony (ABC) Optimization is used in conjunction with the thresholding process to remove tumors from brain MRI scans. Another optimization approach is used to recover the learning rate of the Convolutional Neural Network for the final hybrid classification. The experiment model is assessed by using the multimodal brain tumor (BRATS) 2013 and 2015 challenge datasets from medical image computing. The outcomes of the experiment presented that the method achieved the segmentation 95.23% and 94% of accuracy, where the proposed optimized CNN achieved classification accuracy of 98.5% and 99% for both datasets.


Fernandes SL, Tanik UJ, Rajinikanth V, Karthik KA. A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Computing and Applications. 2020 Oct;32(20):15897-908.


Khan MA, Akram T, Sharif M, Saba T, Javed K, Lali IU, Tanik UJ, Rehman A. Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion. Microscopy research and technique. 2019 Jun;82(6):741-63.


Sharif, M. I., Li, J. P., Khan, M. A., &Saleem, M. A. (2020). Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.


Amin, J., Sharif, M., Yasmin, M., and Fernandes, S.L., A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 2017

Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., and Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical image analysis, 44, 1-13


Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.


Rajinikanth, V., Satapathy, S. C., Fernandes, S. L., and Nachiappan, S., Entropy based segmentation of tumor from brain MR images-a study with teaching learning based optimization. Pattern Recogn. Lett. 94:87-95, 2017.


Upadhyay, N., and AJTBjor, W., Conventional MRI evaluation of gliomas. 84 (special_issue_2):S107-S111, 2011.


Nida, N., Sharif, M., Khan, M. U. G., Yasmin, M., and Fernandes, S. L., A framework for automatic colorization of medical imaging. IIOAB J. 7:202-209, 2016.

Gordillo, N., Montseny, E., and Sobrevilla, P.J., State of the art survey on MRI brain tumor segmentation. 31 (7):1426-1438, 2013.


Zhang, L., Song, M., Liu, X., Bu, J., and Chen, C.J.S.P., Fast multiview segment graph kernel for object classification. 93 (6):1597- 1607, 2013.


Adams, R., and Bischof, L.J., ITopa, intelligence m. Seeded region growing. 16 (6):641-647, 1994.


Han, J., Quan, R., Zhang, D., and Nie, F.J.I., ToIP Robust object cosegmentation using background prior. 27 (4):1639-1651, 2018.


Raja, N.S.M., Fernandes, S., Dey, N., Satapathy, S.C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing:1-12, 2018.


Rajinikanth, V., Fernandes, S.L., Bhushan, B., and Sunder, N.R., Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Proceedings of 2nd international conference on microelectronics, electromagnetics and telecommunications. Springer, 313-321, 2018.


Deng, W., Xiao, W., Deng, H., and Liu, J., MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. Biomedical engineering and informatics (BMEI), 2010 3rd international conference on, IEEE. 393-396, 2010.


Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., and Tian, QJIToIP., Discovering discriminative graphlets for aerial image categories recognition. 22 12:5071-5084, 2013.


Menze, B.H., Van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., and Golland, P., A generative model for brain tumor segmentation in multi-modal images. International conference on medical image computing and computer-assisted intervention, Springer. 151-159, 2010.


Akram T, Khan MA, Sharif M, Yasmin M. Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features. Journal of Ambient Intelligence and Humanized Computing. 2018 Sep 24:1-20.


Ariyo, O., Zhi-guang, Q., &Tian, L. (2017). Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means. DEStech Transactions on Computer Science and Engineering (csae).


Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., Lali, M. I. U., &Javed, M. Y. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150, 220-234


Brain Tumor Detection. International Arab Journal of Information Technology (IAJIT), 12(1).

Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging, 35(50)1240-1251.


Bahadure, N. B., Ray, A. K., &Thethi, H. P. (2018). Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm. Journal of digital imaging, 1-13.


Sharma, M., Purohit, G., & Mukherjee, S. (2018). Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN) Networking communication and data knowledge engineering (pp. 145-157): Springer.


Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. Inannual conference on medical image understanding and analysis 2017 Jul 11 (pp. 506-517). Springer, Cham.


Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Rueckert D, Glocker B. DeepMedic for brain tumor segmentation. InInternational workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries 2016 Oct 17 (pp. 138-149). Springer, Cham.


Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., and Lladó, X., Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: A review. Artificial intelligence in medicine, 2018.


Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27.


Hai J, Qiao K, Chen J, Tan H, Xu J, Zeng L, Shi D, Yan B. Fully convolutional densenet with multiscale context for automated breast tumor segmentation. Journal of healthcare engineering. 2019 Jan 14;2019.


Zahari Abu Bakar, NooritawatiMd Tahir, Ihsan M Yassin, "Classification Of Parkinson's Disease Based On Multilayer Perceptrons Neural Network', Ieee Colloquium In Signal Processing And Its Applications (Cspa), 2010.


Cordier, N., Menze, B., Delingette, H., &Ayache, N. (2013). Patch-based segmentation of brain tissues. Paper presented at the MICCAI challenge on multimodal brain tumor segmentation.

Reza, S. M., Mays, R., &Iftekharuddin, K. M. (2015). Multi- fractal detrended texture feature for brain tumor classification. Paper presented at the Medical Imaging 2015: Computer-Aided Diagnosis.


Abbasi, S., & Tajeripour, F. (2017). Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing, 219, 526-535.


Hussain, UmairaNazar, Muhammad Attique Khan, IkramUllhaLali, KashifJaved, Imran Ashraf, Junaid Tariq, Hashim Ali, and Ahmad Din. "A Unified design of ACO and skewness based brain tumor segmentation and classification from MRI scans." Journal of Control Engineering and Applied Informatics 22, no. 2 (2020): 43-55.

Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images. IEEE Trans. Med. Imaging. 35, 1240-1251 (2016).


Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with Deep Neural Networks. Med. Image Anal. 35, 18-31 (2016).


Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61-78 (2017).


Dash, S.C.B., Mishra, S.R., Srujan Raju, K. et al. Human action recognition using a hybrid deep learning heuristic. Soft Comput 25, 13079-13092 (2021). https://doi.org/10.1007/s00500-021-06149-7


Wang, T., Lu, C., Shen, G. and Hong, F., 2019. Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network. PeerJ, 7, p.e7731.


B. Padmaja, P. V. Narasimha Rao, M. Madhu Bala and E. K. Rao Patro, "A Novel Design of Autonomous Cars using IoT and Visual Features," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 18-21, doi: 10.1109/I-SMAC.2018.8653736.


Kalyani, G., Janakiramaiah, B., Karuna, A. et al. Diabetic retinopathy detection and classification using capsule networks. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00318-9


M. A. Khan, I. U. Lali, A. Rehman, M. Ishaq, M. Sharif, T. Saba, et al., "Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection," Microscopy research and technique, vol. 82, pp. 909- 922, 2019.


Sharif, M.I., Li, J.P., Khan, M.A. and Saleem, M.A., 2020. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, pp.181-189.


Ramu, G. A secure cloud framework to share EHRs using modified CP-ABE and the attribute bloom filter. Educ Inf Technol 23, 2213-2233 (2018). https://doi.org/10.1007/s10639-018-9713-7


D. Ravi, H. Fabelo, G. M. Callico, and G. Yang, "Manifold Embedding and Semantic Segmentation for Intraoperative Guidance with Hyperspectral Brain Imaging", IEEE Transactions on Medical Imaging, Vol.36, No.9, pp.1845-1857, 2017.


A. Raju, P. Ratna, Suresh, and R. Rajeswara Rao, "Bayesian HCS-based multi-SVNN: A classification approach for brain tumour segmentation and classification using Bayesian fuzzy clustering", Biocybernetics and Biomedical Engineering, Vol.38, No.3, pp.646- 660, 2018.


Kalyani, G., Janakiramaiah, B., Prasad, L.V.N. et al. Efficient crowd counting model using feature pyramid network and ResNeXt. Soft Comput 25, 10497-10507 (2021). https://doi.org/10.1007/s00500-021-05993-x


M. Soltaninejad, G. Yang, T. Lambrou, N. Allinson, T. L. Jones, T. R. Barrick, F. A. Howe, and X. Ye, "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, No.2, pp.183- 203, 2017.


Additional Files



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.