ResNet18 supported inspection of tuberculosis in chest radiographs with integrated deep, lbp, and DWT features

  1. Venkatesan Rajinikanth 1
  2. Seifedine Kadry 2
  3. Pablo Moreno Ger 3
  1. 1 Saveetha School of Engineering
  2. 2 Noroff University College
  3. 3 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

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Revista:
IJIMAI

ISSN: 1989-1660

Any de publicació: 2023

Volum: 8

Número: 2

Pàgines: 38-46

Tipus: Article

DOI: 10.9781/IJIMAI.2023.05.004 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Altres publicacions en: IJIMAI

Objectius de Desenvolupament Sostenible

Resum

The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers.

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Fuente de los datos: Dialnet