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

Mostrar afiliaciones +
Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 38-46

Tipo: Artículo

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

Otras publicaciones en: IJIMAI

Objetivos de desarrollo sostenible

Resumen

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.

Referencias bibliográficas

  • S. Arunmozhi, , A. P. Kamath, V. Rajinikanth, . Detection of Tuberculosis in Chest X-Ray using Cancatinated Deep and Handcrafted Features. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-4). IEEE, 2021.
  • L. Ma, Y.Wang, L.Guo, Y.Zhang, P.Wang, X.Pei, et al.Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning. Journal of X-Ray Science and Technology, (Preprint), 1-13, 2020.
  • M. Nijiati, Z. Zhang, A. Abulizi, H.Miao, A.Tuluhong, S.Quan, et al. Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings. Journal of X-Ray Science and Technology, (Preprint), 1-12, 2021.
  • X. Wang, Z. Zhang, D. Chen, N. Peng, P. U. Thakker, M. Z. Schwartz, Y. Zhang, Challenges in the diagnosis of testicular infarction in the presence of prolonged epididymitis: Three cases report and literature review. Journal of X-Ray Science and Technology, 28(4), 809-819, 2020.
  • K. Zaman, Tuberculosis: a global health problem. Journal of health, population, and nutrition, 28(2), 111, 2010.
  • WHO (https://www.who.int/news-room/fact-sheets/detail/tuberculosis)
  • E. Priya, Optimization-Based Tuberculosis Image Segmentation by Ant Colony Heuristic Method. International Journal of Swarm Intelligence Research (IJSIR), 13(1), 1-24, 2022.
  • E. Priya, S. Srinivasan, Automated decision support system for tuberculosis digital images using evolutionary learning machines. European Journal of Biomedical Informatics, 9(2), 2013.
  • S. Arunmozhi, V. Rajinikanth, M. P. Rajakumar, . Deep-Learning based Automated Detection of Pneumonia in Chest Radiographs. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-4). IEEE, 2021.
  • A. Aziz, M. Attique, U.Tariq, U., Y. Nam, M. Nazir, C. W. Jeong et al., . An Ensemble of Optimal Deep Learning Features for brain tumor classification. doi:10.32604/cmc.2021.018606, 2021
  • A. Bhandary, G. A. Prabhu, V. Rajinikanth, VK. P. Thanaraj, , S. C. Satapathy, ., D. E. Robbins, Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters, 129, 271-278, 2020.
  • X. Chen, X. Wang, K. Zhang, K., R. Zhang, K. M. Fung, T. C. Thai, et al.. Recent advances and clinical applications of deep learning in medical image analysis. arXiv preprint arXiv:2105.13381, 2021.
  • V. Chouhan, S. K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, et al.A noveltransfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559, 2020.
  • N. Dey, Y. D. Zhang, Y. D., V. Rajinikanth, R. Pugalenthi, N. S. M. Raja. Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recognition Letters, 143, 67-74, 2021.
  • U. Raghavendra, A., Gudigar, T. N. Rao,V. Rajinikanth, E. J. Ciaccio, , C. H. Yeong, et al. Feature‐versus deep learning‐based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study. International Journal of Imaging Systems and Technology, 2021.
  • V. Rajinikanth, S. M. Aslam, S. Kadry, Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities. Symmetry, 13(11), 2080, 2021.
  • V. Rajinikanth, S. Kadry, Y. Nam, Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices. Information Technology and Control, 50(2), 342-356, 2021.
  • M. Ramzan, M. Raza, M. Sharif, M. A. Khan, Y. Nam, Y. . Gastrointestinal Tract Infections Classification Using Deep Learning. Cmc-Computers Materials & Continua DOI:10.32604/cmc.2021.015920, 2021.
  • T. Rahman, A. Khandakar, M. A. Kadir, K. R. Islam, K. F. Islam, R. Mazhar, et al. Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access, 8(, 191586-191601, 2020.
  • M. P. Rajakumar, R. Sonia, B. U. Maheswari, S. P. Karuppiah, Tuberculosis detection in chest X-ray using Mayfly-algorithm optimized dual-deeplearning features. Journal of X-Ray Science and Technology, 29, 961-974, 2021.
  • M. Odusami, R. Maskeliunas, R. Damaševičius, S. Misra, Comparable Study of Pre-trained Model on Alzheimer Disease Classification. In International Conference on Computational Science and Its Applications (pp. 63-74). Springer, Cham, 2021.
  • A. Afzali, F. B. Mofrad, ., M. Pouladian, . Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description. Medical & biological engineering & computing, 58, 1965-1986. 2020.
  • S. Jaeger, S. Candemir, S. Antani, Y.X.J. Wáng, P.X. Lu, P., & Thoma, Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery, 4(6), 475, 2014.
  • M. H. A. Hijazi, S. K. T. Hwa, A. Bade, R. Yaakob, M. S. Jeffree, Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images, IAES International Journal of Artificial Intelligence, 8(2019), 429, 2019.
  • R. Hooda, S.Sofat, S.Kaur, A.Mittal, F. Meriaudeau, F, Deep-learning: A potential method for tuberculosis detection using chest radiography, In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) 497-502. IEEE, 2017.
  • TB Data. DOI:10.21227/mps8-kb56.
  • A. Rohilla, R. Hooda, A. Mittal,TB detection in chest radiograph using deep learning architecture, ICETETSM-17, 136-147, 2017.
  • S. Kadry, G. Srivastava, V. Rajinikanth, S. Rho, Y. Kim, Tuberculosis detection in chest radiographs using spotted hyena algorithm optimized deep and handcrafted features. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9263379
  • R. Mohan, S., Kadry, V. Rajinikanth, A. Majumdar, O. Thinnukool, Automatic Detection of Tuberculosis Using VGG19 with Seagull- International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 8, Nº2 - 46 - Algorithm. Life, 12(11), 1848, 2022.
  • S. Kadry, V.Rajinikanth, R.González Crespo, E. Verdú, Automated detection of age-related macular degeneration using a pre-trained deeplearning scheme. The Journal of Supercomputing, 1-20, 2021.
  • M. A. Khan, A.Majid, N.Hussain, M. Alhaisoni, Y. D.Zhang, S.Kadry, Y.Nam,Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization., 2021
  • M. A. Khan, V.Rajinikanth, S. C.Satapathy, D.Taniar, J. R. Mohanty, U.Tariq, R.Damaševičius, VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics, 11(12), 2208, 2021.
  • M. A. Khan, M. Sharif, T.Akram, R.Damaševičius, R.Maskeliūnas, Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics, 11(5), 811, 2021.
  • S. Kaliyugarasan, A. Lundervold, A. S. Lundervold, Pulmonary nodule classification in lung cancer from 3D thoracic CT scans using fastai and MONAI. International Journal of Interactive Multimedia and Artificial Intelligence, 6(7), 83-89. DOI: 10.9781/ijimai.2021.05.002, 2021.
  • V. Srivastava, S. Gupta, G.Chaudhary, G., A. Balodi, M. Khari, V. GarcíaDíaz, An enhanced texture-based feature extraction approach for classification of biomedical images of CT-scan of lungs. International Journal of Interactive Multimedia and Artificial Intelligence, 6(7), 18-25. DOI: 10.9781/ijimai.2020.11.003, 2021.
  • A. A. Rezaie, A. Habiboghli, Detection of lung nodules on medical images by the use of fractal segmentation. International Journal of Interactive Multimedia and Artificial Intelligence, 4(5), 15-19. DOI: 10.9781/ ijimai.2017.452, 2017.
  • V. C. Osamor, A. A. Azeta, O. O. Ajulo, Tuberculosis–Diagnostic Expert System: An architecture for translating patients information from the web for use in tuberculosis diagnosis. Health informatics journal, 20(4), 275-287, 2014.
  • M. A. Khemchandani, S. M. Jadhav, B. R. Iyer, Brain tumor segmentation and identification using particle imperialist deep convolutional neural network in MRI images. International Journal of Interactive Multimedia and Artificial Intelligence, 7(7), 38-47. DOI: 10.9781/ijimai.2022.10.006, 2022.
  • G. R. Vásquez-Morales, S. M. Martinez-Monterrubio, P. Moreno-Ger, J. A. Recio-Garcia, Explainable prediction of chronic renal disease in the Colombian population using neural networks and case-based reasoning. IEEE Access, 7, 152900-152910, 2019.
  • A. Gudigar, U. Raghavendra, T. Devasia, K. Nayak, S. M. Danish, G. Kamath, et al. Global weighted LBP based entropy features for the assessment of pulmonary hypertension. Pattern Recognition Letters, 125, 35-41, 2019.
  • S. Mirniaharikandehei, M. Heidari, G.Danala, S. Lakshmivarahan, B.A novel feature reduction method to improve performance of machine learning model. In Medical Imaging 2021: Computer-Aided Diagnosis (Vol. 11597, p. 1159726). International Society for Optics and Photonics, 2021.
  • N. Sri Madhava Raja, V. Rajinikanth, K. Latha, K. (2014). Otsu based optimal multilevel image thresholding using firefly algorithm. Modelling and Simulation in Engineering, 2014.
  • M. I. Waly, M. Y. Sikkandar, M. A. Aboamer, S. Kadry, O. Thinnukool, Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model. Cmc-Computers Materials & Continua, 70(2), 3295-3309, 2022.
Fuente de los datos: Dialnet