تشخیص خودکار بیماری های ریوی با استفاده از ویژگی های مبتنی بر تبدیل کسینوسی گسسته در تصاویر رادیوگرافی
الموضوعات :
1 - دانشگاه محقق اردبیلی
2 - دانشگاه محقق اردبیلی
الکلمات المفتاحية: آنالیز تشخیصي حساس به مکان, تبدیل کسینوسي گسسته, تبدیل موجک گسسته, تشخیص بیماريهاي ریوي بینابیني, تصاویر رادیوگرافي, درخت تصمیم,
ملخص المقالة :
استفاده از نتایج خام رادیوگرافي در تشخیص بیماريهاي ریوي عملکرد قابلقبولي ندارد. یادگیري ماشین ميتواند به تشخیص دقیقتر بیماريها کمک کند. مطالعات گستردهاي در حوزه تشخیص خودکار بیماريها با کمک یادگیري ماشین کلاسیک و عمیق انجام شده؛ اما این روشها دقت و کارایي قابلقبولي ندارند یا به دادههاي یادگیري زیادي نیاز دارند. براي مقابله با این چالشها، در این مقاله، روش جدیدي براي تشخیص خودکار بیماريهاي ریوي بینابیني در تصاویر رادیوگرافي ارائه ميشود. در گام اول، اطلاعات بیمار از تصاویر حذف شده؛ سپس، پیکسلهاي باقیمانده، جهت پردازشهاي دقیقتر، استانداردسازي ميشوند. در گام دوم، پایایي روش پیشنهادي با کمک تبدیل رادان بهبود یافته، دادههاي اضافي با استفاده از فیلتر Top-hat حذف شده و نرخ تشخیص با بهرهبرداري از تبدیل موجک گسسته و تبدیل کسینوسي گسسته افزایش ميیابد. سپس، تعداد ویژگيهاي نهایي با کمک آنالیز تشخیصي حساس به مکان کاهش ميیابد. در گام سوم، تصاویر پردازششده به دو دسته یادگیري و تست تقسیم ميشوند؛ با استفاده از دادههاي یادگیري، مدلهاي مختلفي ایجاد شده و با کمک دادههاي تست، بهترین مدل انتخاب ميشود. نتایج شبیهسازيها بر روي مجموعه داده NIH نشان ميدهد که روش پیشنهادي مبتني بر درخت تصمیم با بهبود میانگین هارمونیک حساسیت و صحت تا 08 / 1 برابر، دقیقترین مدل را ارائه ميدهد.
المصادر:
منابع و مأخذ
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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.
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[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.
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[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.
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[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.
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[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.
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[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.
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