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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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://www.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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