Optimization of Image Analysis Algorithm for the Diagnosis of Brain Tumors

Authors

  • Mukund Kulkarni Research Scholar, Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune
  • Rajesh Jalnekar Professor and Director, Vishwakarma Institute of Technology, Pune

Keywords:

Artificial Neural Network (ANN), Carcinoma, Diagnostics, MR Images, Particle Swarm Optimization (PSO), Segmentation, Tumor

Abstract

Image analysis is the best diagnostics tool for carcinoma cell diagnosis. Symptomatic clinical correlation with pathological findings amalgamated with Image analysis is the best way to diagnose cancerous cells and their nature. The worldwide brain tumor recorded cases have risen in recent years which is a huge challenge for clinicians to detect the tumor in the early stage of development. Though, plenty of research has been conducted on brain MR image segmentation and features extraction, but the existing approaches comprise certain limitations. These approaches are not restricted to massive time consumption, the lower rate of accuracy along with the more computational cost is also challenging for current scenario. In this research, a novel and optimized image analysis algorithm for the diagnosis of brain tumors has been presented. This optimized image analysis algorithm is rooted in the combined artificial neural network (ANN) as well as particle swarm optimization (PSO) approach. The obtained results have been recorded enhanced in terms of accuracy as well as sensitivity in comparison to the existing brain tumor detection methods. Using the combined approach i.e., ANN and PSO methods, the accuracy, sensitivity as well as recorded time was optimal. The proposed optimized image analysis algorithm takes very less time in operation i.e., only 0.8 seconds which is minimal in comparison with earlier methods. The measured accuracy and sensitivity for the proposed optimized image analysis algorithm on white matter (WM),  gray matter (GM), and tumor MR images are found 98%, 97%, and 99% as well as 97.5%, 98.4%, and 99.22%, respectively.

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References

V. Ntziachristos, M. A. Pleitez, S. Aime, and K. M. Brindle, “Emerging Technologies to Image Tissue Metabolism,” Cell Metabolism, vol. 29, no. 3, pp. 518-538, 2019. doi: 10.1016/j.cmet.2018.09.004.

S. Gudise, G. B. Kande, and T. S. Savithri, “MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm,” Int. J. Image Graph., vol. 21, no. 3, 2021, doi: 10.1142/S0219467821500340.

S. Pontes-Filho, A. G. Dahl, S. Nichele, and G. B. M. e. Mello, “A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents,” in Lecture Notes in Networks and Systems, 2022. doi: 10.1007/978-3-030-82199-9_36.

M. K. Abd-Ellah, A. I. Awad, A. A. M. Khalaf, and H. F. A. Hamed, “A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned,” Magnetic Resonance Imaging., vol. 61, pp. 300-318, 2019. doi: 10.1016/j.mri.2019.05.028.

C. E. Zimmerman et al., “Automatic Segmentation of Bone Selective MR Images for Visualization and Craniometry of the Cranial Vault,” Acad. Radiol., 2021, doi: 10.1016/j.acra.2021.03.010.

H. Sajedi and N. Pardakhti, “Age Prediction Based on Brain MRI Image: A Survey,” Journal of Medical Systems. 2019. doi: 10.1007/s10916-019-1401-7.

I. Oksuz, “Brain MRI artefact detection and correction using convolutional neural networks,” Comput. Methods Programs Biomed., 2021, doi: 10.1016/j.cmpb.2020.105909.

Z. Zhang, J. Li, C. Tian, Z. Zhong, Z. Jiao, and X. Gao, “Quality-driven deep active learning method for 3D brain MRI segmentation,” Neurocomputing, vol. 446, pp. 106-117, 2021, doi: 10.1016/j.neucom.2021.03.050.

Z. Jiang, X. Lyu, J. Zhang, Q. Zhang, and X. Wei, “Review of deep learning methods for MRI brain tumor image segmentation,” Journal of Image and Graphics. 2020. doi: 10.11834/jig.190173.

I. Abd El Kader et al., “Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model,” Diagnostics, vol. 11, no. 9, 2021, doi: 10.3390/diagnostics11091589.

A. Fawzi, A. Achuthan, and B. Belaton, “Brain image segmentation in recent years: A narrative review,” Brain Sciences. 2021. doi: 10.3390/brainsci11081055.

Y. Zhang, S. Wang, G. Ji, and Z. Dong, “An MR brain images classifier system via particle swarm optimization and kernel support vector machine,” Sci. World J., 2013, doi: 10.1155/2013/130134.

M. Ahmadi, A. Sharifi, M. Jafarian Fard, and N. Soleimani, “Detection of brain lesion location in MRI images using convolutional neural network and robust PCA,” Int. J. Neurosci., 2021, doi: 10.1080/00207454.2021.1883602.

D. Filatov and G. N. A. H. Yar, “Brain Tumor Diagnosis and Classification via Pre-Trained Convolutional Neural Networks,” 2022, [Online]. Available: http://arxiv.org/abs/2208.00768

V. Sheejakumari and B. Sankara Gomathi, “MRI brain images healthy and pathological tissues classification with the aid of improved particle swarm optimization and neural network,” Comput. Math. Methods Med., 2015, doi: 10.1155/2015/807826.

M. M. Badža and M. C. Barjaktarović, “Classification of brain tumors from mri images using a convolutional neural network,” Appl. Sci., vol. 10, no. 6, 2020, doi: 10.3390/app10061999.

A. K. Sharma, A. Nandal, A. Dhaka, D. Koundal, D. C. Bogatinoska, and H. Alyami, “Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection,” Biomed Res. Int., 2022, doi: 10.1155/2022/7348344.

V. Sivakumar and N. Janakiraman, “A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA,” BioSystems, vol. 198, 2020, doi: 10.1016/j.biosystems.2020.104226.

I. Aboussaleh, J. Riffi, A. M. Mahraz, and H. Tairi, “Brain tumor segmentation based on deep learning’s feature representation,” J. Imaging, vol. 7, no. 12, 2021, doi: 10.3390/jimaging7120269.

R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi, S. Anari, M. Naseri, and M. Bendechache, “Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images,” Sci. Rep., 2021, doi: 10.1038/s41598-021-90428-8.

Flow chart of our proposed image analysis algorithm for the diagnosis of brain tumors

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Published

16.04.2023

How to Cite

Kulkarni, M. ., & Jalnekar, R. . (2023). Optimization of Image Analysis Algorithm for the Diagnosis of Brain Tumors . International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 325 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2796

Issue

Vol. 11 No. 5s (2023): Special Issue on Applications of Advanced Engineering Technologies

Section

Research Article

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