Automatic Lung Diseases Identification using Discrete Cosine Transform-based Features in Radiography Images
Subject Areas : ICT
Shamim Yousefi
1
,
Samad Najjar-Ghabel
2
1 - University of Mohaghegh Ardabili.Ardabil.Iran
2 - University of Mohaghegh Ardabili
Keywords: Locality Sensitive Discriminant Analysis, Discrete Wavelet Transform, Discrete Cosine Transform, Interstitial lung disease identification, Radiography images, Decision tree,
Abstract :
The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches.
منابع و مأخذ
[1] A. L. Olson, P. Patnaik, N. Hartmann, R. L. Bohn, E. M. Garry, and L. Wallace, “Prevalence and Incidence of Chronic Fibrosing Interstitial Lung Diseases with a Progressive Phenotype in the United States Estimated in a Large Claims Database Analysis,” Advances in Therapy, vol. 38, no. 7, pp. 4100–4114, Jul. 2021.
[2] L. Sesé et al., “Adult interstitial lung diseases and their epidemiology,” La Presse Médicale, vol. 49, no. 2, p. 104023, Jun. 2020.
[3] J. Salonen, M. Purokivi, R. Bloigu, and R. Kaarteenaho, “Prognosis and causes of death of patients with acute exacerbation of fibrosing interstitial lung diseases,” BMJ Open Respiratory Research, vol. 7, no. 1, p. e000563, Apr. 2020.
[4] K. K. Brown et al., “The natural history of progressive fibrosing interstitial lung diseases,” European Respiratory Journal, vol. 55, no. 6, p. 2000085, Jun. 2020.
[5] F. Liu et al., “The application of artificial intelligence to chest medical image analysis,” Intelligent Medicine, Jul. 2021.
[6] A. A. Peters et al., “Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists,” Journal of Thoracic Disease, vol. 13, no. 5, pp. 2728–2737, May 2021.
[7] E. Matsuyama, “A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images,” Journal of Biomedical Science and Engineering, vol. 14, no. 06, pp. 288–299, 2021.
[8] J. Rasheed, A. A. Hameed, C. Djeddi, A. Jamil, and F. Al-Turjman, “A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images,” Interdisciplinary Sciences: Computational Life Sciences, vol. 13, no. 1, pp. 103–117, Mar. 2021.
[9] R. Zhang et al., “Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence,” Radiology, vol. 298, no. 2, pp. E88–E97, Feb. 2021.
[10] T. Kwon et al., “Diagnostic performance of artificial intelligence model for pneumonia from chest radiography,” PLOS ONE, vol. 16, no. 4, p. e0249399, Apr. 2021.
[11] W. Khan, N. Zaki, and L. Ali, “Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 51747–51771, 2021.
[12] A. Olson et al., “Estimation of the Prevalence of Progressive Fibrosing Interstitial Lung Diseases: Systematic Literature Review and Data from a Physician Survey,” Advances in Therapy, vol. 38, no. 2, pp. 854–867, Feb. 2021.
[13] S. T. H. Kieu, A. Bade, M. H. A. Hijazi, and H. Kolivand, “A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions,” Journal of Imaging, vol. 6, no. 12, p. 131, Dec. 2020.
[14] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evolutionary Intelligence, Jan. 2021.
[15] J. Ma, Y. Song, X. Tian, Y. Hua, R. Zhang, and J. Wu, “Survey on deep learning for pulmonary medical imaging,” Frontiers of Medicine, vol. 14, no. 4, pp. 450–469, Aug. 2020.
[16] S. Chen and S. Wu, “Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data,” Journal of Medical Internet Research, vol. 22, no. 3, p. e17695, Mar. 2020.
[17] U. R. Acharya et al., “Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index,” Computers in Biology and Medicine, vol. 84, pp. 59–68, May 2017.
[18] S. Bharati, P. Podder, and M. R. H. Mondal, “Hybrid deep learning for detecting lung diseases from X-ray images,” Informatics in Medicine Unlocked, vol. 20, p. 100391, 2020.
[19] “NIH sample Chest X-rays dataset,” 2022. [Online]. Available: https://www.kaggle.com/nih-chest-xrays/sample,.
[20] L. L. G. Oliveira, S. A. e Silva, L. H. V. Ribeiro, R. M. de Oliveira, C. J. Coelho, and A. L. S. S. Andrade, “Computer-aided diagnosis in chest radiography for detection of childhood pneumonia,” International Journal of Medical Informatics, vol. 77, no. 8, pp. 555–564, Aug. 2008.
[21] J. G. Greener, S. M. Kandathil, L. Moffat, and D. T. Jones, “A guide to machine learning for biologists,” Nature Reviews Molecular Cell Biology, Sep. 2021.
[22] S. Yousefi, F. Derakhshan, and H. Karimipour, “Applications of Big Data Analytics and Machine Learning in the Internet of Things,” in Handbook of Big Data Privacy, Cham: Springer International Publishing, 2020, pp. 77–108.
[23] E. Yahaghi, M. Mirzapour, and A. Movafeghi, “Comparison of traditional and adaptive multi-scale products thresholding for enhancing the radiographs of welded object,” The European Physical Journal Plus, vol. 136, no. 7, p. 744, Jul. 2021.
[24] Y. Dong, X. Ma, and T. Fu, “Electrical load forecasting: A deep learning approach based on K-nearest neighbors,” Applied Soft Computing, vol. 99, p. 106900, Feb. 2021.
[25] A. Khatri, R. Jain, H. Vashista, N. Mittal, P. Ranjan, and R. Janardhanan, “Pneumonia Identification in Chest X-Ray Images Using EMD,” 2020, pp. 87–98.
[26] R. V. Adiraju, K. K. Masanipalli, T. D. Reddy, R. Pedapalli, S. Chundru, and A. K. Panigrahy, “An extensive survey on finger and palm vein recognition system,” Materials Today: Proceedings, vol. 45, pp. 1804–1808, 2021.
[27] S. Varela-Santos and P. Melin, “Classification of X-Ray Images for Pneumonia Detection Using Texture Features and Neural Networks,” 2020, pp. 237–253.
[28] S. Aouat, I. Ait-hammi, and I. Hamouchene, “A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix,” Multimedia Tools and Applications, vol. 80, no. 16, pp. 24027–24052, Jul. 2021.
[29] M. Kubat, “Artificial Neural Networks,” in An Introduction to Machine Learning, Cham: Springer International Publishing, 2021, pp. 117–143.
[30] P. Chhikara, P. Singh, P. Gupta, and T. Bhatia, “Deep Convolutional Neural Network with Transfer Learning for Detecting Pneumonia on Chest X-Rays,” 2020, pp. 155–168.
[31] H. Song, xiu-ying Han, C. E. Montenegro-Marin, and S. Krishnamoorthy, “Secure prediction and assessment of sports injuries using deep learning based convolutional neural network,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3399–3410, Mar. 2021.
[32] M. Yildirim, “Analog circuit implementation based on median filter for salt and pepper noise reduction in image,” Analog Integrated Circuits and Signal Processing, vol. 107, no. 1, pp. 195–202, Apr. 2021.
[33] A. Kumar, R. K. Jha, and N. K. Nishchal, “An improved Gamma correction model for image dehazing in a multi-exposure fusion framework,” Journal of Visual Communication and Image Representation, vol. 78, p. 103122, Jul. 2021.
[34] G. Ulutas and B. Ustubioglu, “Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation,” Multimedia Tools and Applications, vol. 80, no. 10, pp. 15067–15091, Apr. 2021.
[35] M. S. El_Tokhy, “Development of optimum watermarking algorithm for radiography images,” Computers & Electrical Engineering, vol. 89, p. 106932, Jan. 2021.
[36] S. Thakur, Y. Goplani, S. Arora, R. Upadhyay, and G. Sharma, “Chest X-Ray Images Based Automated Detection of Pneumonia Using Transfer Learning and CNN,” 2021, pp. 329–335.
[37] G. Liang and L. Zheng, “A transfer learning method with deep residual network for pediatric pneumonia diagnosis,” Computer Methods and Programs in Biomedicine, vol. 187, p. 104964, Apr. 2020.
[38] H. Wu, P. Xie, H. Zhang, D. Li, and M. Cheng, “Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2893–2907, Oct. 2020.
[39] S. Weppler et al., “Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic,” Frontiers in Oncology, vol. 11, Jun. 2021.
[40] R. Sarkar, A. Hazra, K. Sadhu, and P. Ghosh, “A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks,” 2020, pp. 1–12.
[41] E. Yahaghi, M. Mirzapour, A. Movafeghi, and B. Rokrok, “Interlaced bilateral filtering and wavelet thresholding for flaw detection in the radiography of weldments,” The European Physical Journal Plus, vol. 135, no. 1, p. 42, Jan. 2020.
[42] A. Vidyarthi and A. Malik, “A hybridized modified densenet deep architecture with CLAHE algorithm for humpback whale identification and recognition,” Multimedia Tools and Applications, Jul. 2021.
[43] W.-N. Mohd-Isa, J. Joseph, N. Hashim, and N. Salih, “Enhancement of digitized X-ray films using Contrast-Limited Adaptive Histogram Equalization (CLAHE),” F1000Research, vol. 10, p. 1051, Oct. 2021.
[44] D. Ziou, N. Nacereddine, and A. B. Goumeidane, “Scale space Radon transform,” IET Image Processing, vol. 15, no. 9, pp. 2097–2111, Jul. 2021.
[45] O. Ramos-Soto et al., “An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering,” Computer Methods and Programs in Biomedicine, vol. 201, p. 105949, Apr. 2021.
[46] M. Shajahan, S. A. M. Aris, S. Usman, and N. M. Noor, “IRPMID: Medical XRAY Image Impulse Noise Removal using Partition Aided Median, Interpolation and DWT,” in 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2021, pp. 105–110.
[47] X. Wang and X. Chen, “An image encryption algorithm based on dynamic row scrambling and Zigzag transformation,” Chaos, Solitons & Fractals, vol. 147, p. 110962, Jun. 2021.
[48] H. Yao, Y. Zhang, Y. Wei, and Y. Tian, “Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification,” Mathematical Problems in Engineering, vol. 2020, pp. 1–16, Dec. 2020.
[49] S. B. Scott et al., “A Coordinated Analysis of Variance in Affect in Daily Life,” Assessment, vol. 27, no. 8, pp. 1683–1698, Dec. 2020.
[50] H. Lu and X. Ma, “Hybrid decision tree-based machine learning models for short-term water quality prediction,” Chemosphere, vol. 249, p. 126169, Jun. 2020.
[51] H. Saadatfar, S. Khosravi, J. H. Joloudari, A. Mosavi, and S. Shamshirband, “A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning,” Mathematics, vol. 8, no. 2, p. 286, Feb. 2020.
[52] D. A. Pisner and D. M. Schnyer, “Support vector machine,” in Machine Learning, Elsevier, 2020, pp. 101–121.
[53] X. Yang et al., “Research and applications of artificial neural network in pavement engineering: A state-of-the-art review,” Journal of Traffic and Transportation Engineering (English Edition), Oct. 2021.
[54] A. M. Alqudah, S. Qazan, and I. S. Masad, “Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images,” Journal of Medical and Biological Engineering, Jun. 2021.
[55] I. MEJÀRE, H.-G. GRÖNDAHL, K. CARLSTEDT, A.-C. GREVER, and E. OTTOSSON, “Accuracy at radiography and probing for the diagnosis of proximal caries,” European Journal of Oral Sciences, vol. 93, no. 2, pp. 178–184, Apr. 1985.
[56] J. T. Braggio, E. S. Hall, S. A. Weber, and A. K. Huff, “Contribution of AOD-PM2.5 surfaces to respiratory-cardiovascular hospital events in urban and rural areas in Baltimore, Maryland, USA: New analytical method correctly identified true positive cases and true negative controls,” Atmospheric Environment, vol. 262, p. 118629, Oct. 2021.
[57] S. Sahoo, A. Subudhi, M. Dash, and S. Sabut, “Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm,” International Journal of Automation and Computing, vol. 17, no. 4, pp. 551–561, Aug. 2020.
[58] X. XU, W. CHEN, and Y. SUN, “Over-sampling algorithm for imbalanced data classification,” JSEE, vol. 30, no. 6, pp. 1182–1191, 2019.
