Deep convolutional neural network for chronic kidney disease prediction using ultrasound imaging

• Autorzy: Patil Smitha , Choudhary Savita

Identyfikatory

DOI

10.1515/bams-2020-0068

• Języki publikacji: EN

Abstrakty

EN

Objectives: Chronic kidney disease (CKD) is a common disease and it is related to a higher risk of cardiovascular disease and end-stage renal disease that can be prevented by the earlier recognition and diagnosis of individuals at risk. Even though risk factors for CKD have been recognized, the effectiveness of CKD risk classification via prediction models remains uncertain. This paper intends to introduce a new predictive model for CKD using US image. Methods: The proposed model includes three main phases “(1) preprocessing, (2) feature extraction, (3) and classification.” In the first phase, the input image is subjected to preprocessing, which deploys image inpainting and median filtering processes. After preprocessing, feature extraction takes place under four cases; (a) texture analysis to detect the characteristics of texture, (b) proposed highlevel feature enabled local binary pattern (LBP) extraction, (c) area based feature extraction, and (d) mean intensity based feature extraction. These extracted features are then subjected for classification, where “optimized deep convolutional neural network (DCNN)” is used. In order to make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as diversity maintained hybrid whale moth flame optimization (DM-HWM) model. Results: The accuracy of adopted model at 40th training percentage was 44.72, 11.02, 5.59, 3.92, 3.92, 3.57, 2.59, 1.71, 1.68, and 0.42% superior to traditional artificial neural networks (ANN), support vector machine (SVM), NB, J48, NBtree, LR, composite hypercube on iterated random projection (CHIRP), CNN, moth flame optimization (MFO), and whale optimization algorithm (WOA) models. Conclusions: Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

Słowa kluczowe

EN

chronic kidney disease; DCNN; DM-HWM algorithm; LBP features; moth flame optimization

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2021
• Tom: Vol. 17, no. 2
• Strony: 137--163
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Patil Smitha

• Research Scholar, VTU, RC Sir MVIT, Bengaluru, India
• Presidency University, Bengaluru, India

autor

Choudhary Savita

• Sir M Visvesvaraya Institute of Technology, Bangalore, India

Bibliografia

• 1. Legouis D, Jamme M, Galichon P, Provenchère S, Hertig A. Development of a practical prediction score for chronic kidney disease after cardiac surgery. Br J Anaesth 2018;121:1025-33.
• 2. Mora SC, Goicoechea M, Torres E, Verdalles Ú, Luño J. Cardiovascular risk prediction in chronic kidney disease patients. Nefrología 2017;37:293-300.
• 3. Mun PS, Ting HN, Mirhassani SM, Ong TA, Wong CM, Chong YB. Prediction of chronic kidney disease using urinary dielectric properties and support vector machine. J Microw Power Electromagn Energy 2016;50:201-13.
• 4. Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health 2020;2:e295-302.
• 5. Chen Q, Yu J, Rush BM, Stocker SD, Tan RJ, Kim K. Ultrasound super-resolution imaging provides a noninvasive assessment of renal microvasculature changes during mouse acute kidney injury. Kidney Int 2020;98:355-65.
• 6. Chen C-J, Pai T-W, Hsu H-H, Lee C-H, Chen K-S, Chen Y-C. Prediction of chronic kidney disease stages by renal ultrasound imaging. Enterprise Inf Syst 2020;14:178-95.
• 7. Moloney A, Hladunewich M, Manly E, Hui D, Melamed N. The predictive value of sonographic placental markers for adverse pregnancy outcome in women with chronic kidney disease. Pregnancy Hypertens 2020;20:27-35.
• 8. Lin Y-L, Chen S-Y, Lai Y-H, Wang C-H, Kuo C-H, Liou H-H, et al. Serum creatinine to cystatin C ratio predicts skeletal muscle mass and strength in patients with non-dialysis chronic kidney disease. Clin Nutr 2020;39:2435-41.
• 9. Zhu H, Liao J, Zhou X, Hong X, Song D, Hou FF, et al. Tenascin-C promotes acute kidney injury to chronic kidney disease progression by impairing tubular integrity via αvβ6 integrin signaling. Kidney Int 2020;97:1017-31.
• 10. Hesse B, Rovas A, Buscher K, Kusche-Vihrog K, Lukasz A. Symmetric dimethylarginine in dysfunctional high-density lipoprotein mediates endothelial glycocalyx breakdown in chronic kidney disease. Kidney Int 2020;97:502-15.
• 11. Odeh R, Noone D, Bowlin PR, Braga LHP, Lorenzo AJ. Predicting risk of chronic kidney disease in infants and young children at diagnosis of posterior urethral valves: initial ultrasound kidney characteristics and validation of parenchymal area as forecasters of renal reserve. J Urol 2016;196:862-8.
• 12. Kuo C-C, Chang C-M, Liu K-T, Lin W-K, Chiang H-Y, Chung C-W, et al. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit Med 2019;2:1-9.
• 13. George A, Rajakumar BR. On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis. In: Fourth International Conference on Computing, Communications and Networking Technologies, Tiruchengode, India, July 2013. IEEE; 2013. https://doi.org/10.1109/ICCCNT. 2013.6726611.
• 14. Beno MM, Valarmathi IR, Swamy SM, Rajakumar BR. Threshold prediction for segmenting tumour from brain MRI scans. Int J Imag Syst Technol 2014;24:129-37.
• 15. Gracco A, Luca L, Cozzani M, Siciliani G. Assessment of palatal bone thickness in adults with cone beam computerised tomography. Aust Orthod J 2007;23:109.
• 16. Parisi GF, Herman T, Meel ER, Ciet P, Corput MP, Reiss IK, et al. Influence of early growth on childhood lung function assessed by magnetic resolution imaging and spirometry. The Generation R Study. Eur Respir J 2017;50:PA4154.
• 17. Zhang Y-B, Sheng L-T, Wei W, Guo H, Yang H, Min X, et al. Association of blood lipid profile with incident chronic kidney disease: a Mendelian randomization study. Atherosclerosis 2020;300:19-25.
• 18. Chen M, Arcari L, Engel J, Freiwald T, Puntmann VO. Aortic stiffness is independently associated with interstitial myocardial fibrosis by native T1 and accelerated in the presence of chronic kidney disease. IJC Heart Vasc 2019;24:100389.
• 19. Pruijm M, Milani B, Pivin E, Podhajska A, Burnier M. Reduced cortical oxygenation predicts a progressive decline of renal function in patients with chronic kidney disease. Kidney Int 2018; 93:932-40.
• 20. Williams VR, Konvalinka A, Song X, Zhou X, John R, Pei Y, et al. Connectivity mapping of a chronic kidney disease progression signature identified lysine deacetylases as novel therapeutic targets. Kidney Int 2020;98:116-32.
• 21. Rahma AFA, Adams E, Rahma JA, Mata LA, Sloan J. Critical analysis and limitations of resting ankle-brachial index in the diagnosis of symptomatic peripheral arterial disease patients and the role of diabetes mellitus and chronic kidney disease. J Vasc Surg 2020; 71:937-45.
• 22. Akchurin OM. Chronic kidney disease and dietary measures to improve outcomes. Pediatr Clin 2019;66:247-67.
• 23. Mirjalili S. Moth-flame optimization algorithm: a novel natureinspired heuristic paradigm. Knowl Base Syst 2015;89:228-49.
• 24. Mirjalili S, Lewis A. The Whale optimization algorithm. Adv Eng Software 2016;95:51-67.
• 25. Almansour NA, Syed HF, Khayat NR, Altheeb RK, Olatunji SO. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med 2019;109:101-11.
• 26. Khan B, Naseem R, Muhammad F, Abbas G, Kim S. An empirical evaluation of machine learning techniques for chronic kidney disease prophecy. IEEE Access 2020;8:55012-22.
• 27. Bhutani H, Smith V, Rahbari-Oskoui F, Mittal A, Chapman AB. A comparison of ultrasound and magnetic resonance imaging shows that kidney length predicts chronic kidney disease in autosomal dominant polycystic kidney disease. Kidney Int 2015; 88:146-51.
• 28. Qin T, Wu L, Hua Q, Song Z, Liu T. Prediction of the mechanisms of action of Shenkang in chronic kidney disease: a network pharmacology study and experimental validation. J Ethnopharmacol 2020;246:112128.
• 29. Yang W-Q, Mou S, Xu L, Li F-H, Li H-L. Prediction of tubulointerstitial injury in chronic kidney disease using a noninvasive model: combination of renal sonography and laboratory biomarkers. Ultrasound Med Biol 2018;44:941-8.
• 30. Li Q, Wang D, Zhu X, Shen K, Chen Y. Combination of renal apparent diffusion coefficient and renal parenchymal volume for better assessment of split renal function in chronic kidney disease. Eur J Radiol 2018;108:194-200.
• 31. Krishna KD, Akkala V, Bharath R, Rajalakshmi P, Desai UB. Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system. IRBM 2016;37: 189-97.
• 32. Hore S, Chatterjee S, Shaw RK, Dey N, Virmani J. Detection of chronic kidney disease: a NN-GA-based approach. In: Panigrahi B, Hoda M, Sharma V, Goel S, editors. Nature inspired computing. Singapore: Springer; 2018:109-15 pp.
• 33. Sharma K, Virmani J. A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell (IJACI) 2017;8:52-69.
• 34. Chatterjee S, Dzitac S, Sen S, Rohatinovici NC, Dey N, Ashour AS, et al. Hybrid modified Cuckoo search-neural network in chronic kidney disease classification. In: 2017 14th international conference on engineering of modern electric systems (EMES). IEEE; 2017:164-7 pp.
• 35. Li Y, Shi H, Jiao L, Liu R. Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation. Neurocomputing 2012;87:90-8.
• 36. Chaki J, Dey N. Texture feature extraction techniques for image recognition. Singapore: Springer; 2020.
• 37. Samanta S, Ahmed SS, Salem MA-MM, Nath SS, Dey N, Chowdhury SS. Haralick features based automated glaucoma classification using back propagation neural network. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA). Cham: Springer; 2014:351-8 pp.
• 38. Gadkari D. Image quality analysis using GLCM; 2004.
• 39. Fan K-C, Hung T-Y. A novel local pattern descriptor-local vector pattern in high-order derivative space for face recognition. IEEE Trans Image Process 2014;23:2877-91.
• 40. Cun YL, Kavukvuoglu K, Farabet C. Convolutional networks and applications in vision. In: International symposium on circuits and systems, Paris, France. IEEE; 2010:253-6 pp.
• 41. Rao IV, Rao VM. An enhanced whale optimization algorithm for massive MIMO system. J Netw Commun Syst 2019;2:12-22.
• 42. Poluru RK, Kumar RL. Enhancement of ATC by optimizing TCSC configuration using adaptive Moth flame optimization algorithm. J Comput Mech Power Syst Contr 2019;2:1-9.
• 43. Mahesh KM. Workflow scheduling using improved Moth swarm optimization algorithm in cloud computing. Multimed Res 2020;3.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).