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<p>Sinusitis is regarded as s prevalent health issue which is characterized by the inflammation of the mucous membranes lining the paranasal sinuses. If left untreated or recurrent, sinusitis leads to severe complications including loss of smell and vision, meningitis and infection spread to bones or skin. This necessitates advancements in diagnostic tools for the accurate classification of sinusitis leading to timely and effective treatment. The proposed work integrates the abilities of deep learning which in turn shows favorable outcomes over traditional machine learning methods for enhancing the reliability and accuracy of the diagnosis of sinusitis. Initially, the input images are preprocessed in which absolute mean deviation and skewness function are adopted for contrast enhancement. Following this, Recurrent Neural Network (RNN) based segmentation is carried out for delineating the regions of interest (ROI) accurately. Subsequently, Gray Level Co-occurrence Matrix (GLCM) is used for capturing essential textural information and further a hybrid feature selection method combining Stochastic Fractal Search (SFS) with a modified Bat Algorithm (BA) is introduced to optimize the feature set. The proposed Stochastic Fractal Search based Modified Bat Algorithm (SFS-MBA) approach ensures the selection of the most relevant features, enhancing the subsequent classification performance. Finally, the classification is done by Improved Deep Convolutional Neural Network (IDCNN) efficient in learning and generalization characteristics. The entire work is validated using Python and the promising results prove the ability of the proposed strategy in the context of sinusitis diagnosis.</p>

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Advanced Sinusitis Classification Using Deep Learning: A Hybrid Feature Selection Approach with Enhanced Deep CNN

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • P. Maria Jesi,
  • S. Bharathi,
  • S. Kamatchi,
  • R. J. Anandhi

摘要

Sinusitis is regarded as s prevalent health issue which is characterized by the inflammation of the mucous membranes lining the paranasal sinuses. If left untreated or recurrent, sinusitis leads to severe complications including loss of smell and vision, meningitis and infection spread to bones or skin. This necessitates advancements in diagnostic tools for the accurate classification of sinusitis leading to timely and effective treatment. The proposed work integrates the abilities of deep learning which in turn shows favorable outcomes over traditional machine learning methods for enhancing the reliability and accuracy of the diagnosis of sinusitis. Initially, the input images are preprocessed in which absolute mean deviation and skewness function are adopted for contrast enhancement. Following this, Recurrent Neural Network (RNN) based segmentation is carried out for delineating the regions of interest (ROI) accurately. Subsequently, Gray Level Co-occurrence Matrix (GLCM) is used for capturing essential textural information and further a hybrid feature selection method combining Stochastic Fractal Search (SFS) with a modified Bat Algorithm (BA) is introduced to optimize the feature set. The proposed Stochastic Fractal Search based Modified Bat Algorithm (SFS-MBA) approach ensures the selection of the most relevant features, enhancing the subsequent classification performance. Finally, the classification is done by Improved Deep Convolutional Neural Network (IDCNN) efficient in learning and generalization characteristics. The entire work is validated using Python and the promising results prove the ability of the proposed strategy in the context of sinusitis diagnosis.