P. Maria Jesi; V. Kavitha; Vignesh Prasanna Natarajan; Aravinda K.

Optimizing Sinusitis Detection with Harmony Search-grey Wolf Feature Selection and Modified ANN Classification

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Author(s)

P. Maria Jesi ^1,* V. Kavitha ² Vignesh Prasanna Natarajan ³ Aravinda K. ⁴

1. Department of Computer Science and Engineering, Loyola Institute of Technology and Science, Loyola Nagar, Thovalai, Kanyakumari district, India

2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, India

3. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

4. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2026.03.10

Received: 23 Jan. 2026 / Revised: 6 Mar. 2026 / Accepted: 20 Apr. 2026 / Published: 8 Jun. 2026

Index Terms

Gabor Filter, MM, Gray-level Co-occurrence Matrix, Hybrid HAS-GWO, Modified ANN

Abstract

Sinusitis is an inflammation of the paranasal sinus mucosa, which is an infection caused by a bacterium, fungus or virus. Therefore, for earliest and accurate prediction of sinusitis from Computed Tomography (CT) image, this research introduces a novel Artificial Intelligence (AI) based technique. The developed research is initiated with preprocessing using a Gabor filter to improve the quality of an image. After, segmentation using Gaussian Mixture Model (GMM) is exploited for effective isolation of sinus regions affected by inflammation. For acquiring the crucial features from the segmented regions, Gray-Level Co-occurrence Matrix (GLCM) based feature extraction is utilized which offers clinically meaningful features that improve transparency. Consequently, the hybrid Harmony Search Algorithm (HSA)-Grey Wolf Optimizer (GWO) feature selection is utilized to choose the most relevant features. This hybrid method outperforms traditional selection techniques by effectively identifying the most discriminative and non-redundant features, enhancing classification accuracy while reducing computational complexity. For accurate classification of sinusitis into various severity levels, the modified Artificial Neural Network (ANN) is employed. Unlike end-to-end deep learning models, this modular approach allows for fine-grained control at each stage, ensuring that critical medical insights are not lost in abstraction. This structured pipeline allows each phase to be optimized individually, improving transparency, reliability and ultimately, diagnostic performance. The performance of the research is analyzed via python software and it reveals that the developed classifier achieves an accuracy of 96.41%.

Cite This Paper

P. Maria Jesi, V. Kavitha, Vignesh Prasanna Natarajan, Aravinda K., "Optimizing Sinusitis Detection with Harmony Search-grey Wolf Feature Selection and Modified ANN Classification", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.3, pp.152-169, 2026. DOI:10.5815/ijitcs.2026.03.10

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