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Deep Federated Machine Learning-Based Optimization Methods for Liver Tumor Diagnosis: A Review

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Abstract

Computer-aided liver diagnosis helps doctors accurately identify liver abnormalities and reduce the risk of liver surgery. Early diagnosis and detection of liver lesions depend mainly on medical imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). Segmentation and identification of hepatic lesions in these images are very challenging because these images often come with low resolution and severe noise. Many new machine learning and image analysis techniques have been gradually used on this topic, and their performance is still limited. An automatic and accurate model that incorporates tracking, detection, and diagnosis of hepatic lesions in the 3D volumes of CT and MRI is still lacking. This paper aims to review different models for the automatic detection and diagnosis of the hepatic lesion with CT and MRI and discusses the medical background of liver tumors and the standard elements of the CAD liver diagnosis system. In addition, the concept of federated learning has been introduced, and the fused information from multi-modality (CT and MRI) and the robust and complex features that represent liver lesions accurately have been discussed. More specifically, this paper presents a comprehensive study of the latest work on liver tumor detection and diagnosis, which identifies the contributions of these different approaches and the recommendation model suggested for practical use. Furthermore, this paper was intended to encourage researchers from the medical community, image processing, and machine learning community to pay much attention to the use of deep and federated learning, spiking neuron model, bio-inspired optimization algorithms, fuzzy logic, and neutrosophic logic to address the problems of segmentation and prediction/classification for real-time diagnosis.

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Data Availability

Data is available from the authors upon reasonable request.

References

  1. Almotairi KH, Hussein AM, Abualigah L, Abujayyab SK, Mahmoud EH, Ghanem BO, Gandomi AH (2023) Impact of artificial intelligence on COVID-19 pandemic: a survey of image processing, tracking of disease, prediction of outcomes, and computational medicine. Big Data Cogn Comput 7(1):11

    Article  Google Scholar 

  2. Azizi S, Soleimani R, Ahmadi M, Malekan A, Abualigah L, Dashtiahangar F (2022) Performance enhancement of an uncertain nonlinear medical robot with optimal nonlinear robust controller. Comput Biol Med 146:105567

    Article  Google Scholar 

  3. Singh M, Pujar GV, Kumar SA, Bhagyalalitha M, Akshatha HS, Abuhaija B, Gandomi AH (2022) Evolution of machine learning in tuberculosis diagnosis: a review of deep learning-based medical applications. Electronics 11(17):2634

    Article  Google Scholar 

  4. Nadimi-Shahraki MH, Taghian S, Mirjalili S, Abualigah L (2022) Binary aquila optimizer for selecting effective features from medical data: a covid-19 case study. Mathematics 10(11):1929

    Article  Google Scholar 

  5. Shehab M, Abualigah L, Shambour Q, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Gandomi AH (2022) Machine learning in medical applications: a review of state-of-the-art methods. Comput Biol Med 145:105458

    Article  Google Scholar 

  6. Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH (2023) A deep batch normalized convolution approach for improving COVID-19 detection from chest X-ray images. Pathogens 12(1):17

    Article  Google Scholar 

  7. Abualigah L, Diabat A, Sumari P, Gandomi AH (2021) A novel evolutionary arithmetic optimization algorithm for multilevel thresholding segmentation of covid-19 ct images. Processes 9(7):1155

    Article  Google Scholar 

  8. Zhou J, Sun HC, Wang Z, Cong WM, Wang JH, Zeng MS, Han GH (2018) Guidelines for diagnosis and treatment of primary liver cancer in China (2017 Edition). Liver Cancer 7(3):235–260

    Article  Google Scholar 

  9. Wang FS, Fan JG, Zhang Z, Gao B, Wang HY (2014) The global burden of liver disease: the major impact of China. Hepatology 60(6):2099–2108

    Article  Google Scholar 

  10. Lam S, Doran S, Yuksel HH, Altay O, Turkez H, Nielsen J, Mardinoglu A (2020) Addressing the heterogeneity in liver diseases using biological networks. Brief Bioinform. https://doi.org/10.1093/bib/bbaa002

    Article  Google Scholar 

  11. Alzu’bi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M, Abualigah L (2022) Kidney tumor detection and classification based on deep learning approaches: a new dataset in CT scans. J Healthc Eng

  12. Anter AM, Hassenian AE (2018) Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. J Comput Sci 25:376–387

    Article  Google Scholar 

  13. Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2(1):35

    Article  Google Scholar 

  14. Anter AM, Hassenian AE (2018) Normalized multiple features fusion based on PCA and multiple classifiers voting in CT liver tumor recognition. In: Advances in soft computing and machine learning in image processing. Springer, Cham, pp 113–129

  15. Li X, Chen H, Qi X, Dou Q, Fu C, Heng P (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674

    Article  Google Scholar 

  16. Gotra A, Sivakumaran L, Chartrand G, Vu KN, Vandenbroucke-Menu F, Kauffmann C, Tang A (2017) Liver segmentation: indications, techniques and future directions. Insights Imaging 8(4):377–392

    Article  Google Scholar 

  17. Moghbel M, Mashohor S, Mahmud R, Saripan MIB (2018) Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif Intell Rev 50(4):497–537

    Article  Google Scholar 

  18. Erickson BJ, Korfiatis P, Kline TL, Akkus Z, Philbrick K, Weston AD (2018) Deep learning in radiology: does one size fit all? J Am Coll Radiol 15(3):521–526

    Article  Google Scholar 

  19. Liu Z, Song YQ, Sheng VS, Wang L, Jiang R, Zhang X, Yuan D (2019) Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Syst Appl 126:54–63

    Article  Google Scholar 

  20. Vorontsov E, Cerny M, Régnier P, Di Jorio L, Pal CJ, Lapointe R, Tang A (2019) Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases. Radiology 1(2):180014

    Google Scholar 

  21. Yasaka K, Abe O (2018) Deep learning and artificial intelligence in radiology: current applications and future directions. PLoS Med 15(11):e1002707

    Article  Google Scholar 

  22. ElSoud MA, Anter AM (2016) Computational intelligence optimization algorithm based on meta-heuristic social-spider: case study on CT liver tumor diagnosis. Comput Intell 7(4):466–475

    Google Scholar 

  23. Mahjoub MA (2011) Automatic liver segmentation method in CT images. Can J Image Process Comput Vis 2(8):92–95

    Google Scholar 

  24. Dixit V, Pruthi J (2014) Review of image processing techniques for automatic detection of tumor in human liver. Int J Comput Sci Mob Comput 3(3):371–378

    Google Scholar 

  25. Patil S, Udupi VR, Patole D (2013) A robust system for segmentation of primary liver tumor in CT images. Int J Comput Appl 75(13):6–10

    Google Scholar 

  26. Deokar SM, Hambarde SM (2014) Detection of liver cancer in CT scan images. Int J Innov Technol Adapt Manag 1(6):1–4

    Google Scholar 

  27. Mostafa A, Hassanien AE, Hefny HA (2017) Grey wolf optimization-based segmentation approach for abdomen CT liver images. In: Handbook of research on machine learning innovations and trends. IGI Global, pp 562–581

  28. Li C, Wang X, Eberl S, Fulham M, Yin Y, Chen J, Feng DD (2013) A likelihood and local constraint level set model for liver tumor segmentation from ct volumes. IEEE Trans Biomed Eng 60(10):2967–2977

    Article  Google Scholar 

  29. Li X, Luo S, Li J (2013) Liver segmentation from CT image using fuzzy clustering and level set. Int J Signal Inf Process 4(03):36–42

    Google Scholar 

  30. Beichel R, Bornik A, Bauer C, Sorantin E (2012) Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods. Med Phys 39(3):1361–1373

    Article  Google Scholar 

  31. Todoroki Y, Han XH, Iwamoto Y, Lin L, Hu H, Chen YW (2017) Detection of liver tumor candidates from CT images using deep convolutional neural networks. In: International conference on innovation in medicine and healthcare. Springer, Cham, pp 140–145

  32. Soler L, Delingette H, Malandain G, Montagnat J, Ayache N, Koehl C, Marescaux J (2001) Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput Aided Surg 6(3):131–142

    Article  Google Scholar 

  33. Sudhamani MV, Raju GT (2014) Segmentation and classification of tumour in computed tomography liver images for detection, analysis and preoperative planning. Int J Adv Comput Res 4(1):166–171

    Google Scholar 

  34. Gaber T, Ismail G, Anter A, Soliman M, Ali M, Semary N, Snasel V (2015) Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 4254–4257. IEEE

  35. Anter AM, Hassenian AE (2019) CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artif Intell Med 97:105–117

    Article  Google Scholar 

  36. Choudhary A, Moretto N, Ferrarese FP, Zamboni GA (2008) An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors. In: MICCAI workshop, vol 41, no 1, pp 43–94

  37. Campadelli P, Casiraghi E, Lombardi G (2007) Automatic liver segmentation from abdominal CT scans. In: 14th international conference of the EEE on image analysis and processing (ICIAP 2007), pp 731–736‏

  38. Campadelli P, Casiraghi E, Pratissoli S, Lombardi G (2009) Automatic abdominal organ segmentation from CT images. Electron Lett Comput Vis Image Anal 8(1):1–14

    Google Scholar 

  39. Kumar SS, Moni RS (2010) Diagnosis of liver tumor from CT images using curvelet transform. Int J Comput Sci Eng 2(4):1173–1178

    Google Scholar 

  40. Akram MU, Khanum A, Iqbal K (2010) An automated system for liver CT enhancement and segmentation. ICGST-GVIP J 10(4):17–22

    Google Scholar 

  41. Anter AM, El Souod MA, Azar AT, Hassanien AE (2014) A hybrid approach to diagnosis of hepatic tumors in computed tomography images. Int J Rough Sets Data Anal 1(2):31–48

    Article  Google Scholar 

  42. Abdel-massieh NH, Hadhoud MM, Amin KM (2010) A novel fully automatic technique for liver tumor segmentation from CT scans with knowledge-based constraints. In: 10th international conference of the IEEE on intelligent systems design and applications (ISDA2010), pp 1253–1258‏

  43. Mostafa A, Hefny H, Ghali N, Hassanien AE, Schaefer G (2012) Evaluating the effects of image filters in CT Liver CAD system. In: International conference of the IEEE on biomedical and health informatics (BHI 2012), IEEE-EMBS, pp 448–451

  44. Rusko L, Bekes, G, Nemeth G, Fidrich M (2007) Fully automatic liver segmentation for contrast-enhanced CT images. MICCAI Wshp. 3D Segment Clin 2(7):143–150‏

  45. Lim SJ, Jeong YY, Ho YS (2005) Segmentation of the liver using the deformable contour method on CT images. In: Advances in multimedia information processing-PCM 2005. Springer, Berlin, pp 570–581

  46. Yim PJ, Foran DJ (2003) Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms. In: 16th international conference of the IEEE symposium on computer-based medical systems, pp 329–335‏

  47. Liu J, Wang Z, Zhang R (2009) Liver cancer CT image segmentation methods based on watershed algorithm. In: International conference of the IEEE on computational intelligence and software engineering (CiSE2009), pp 1–4

  48. Zidan A, Ghali N, Hassamen AE, Hefny H (2012) Level set-based CT liver image segmentation with watershed and artificial neural networks. In: 12th international conference of the IEEE on hybrid intelligent systems (HIS2012), pp 96–102

  49. Stawiaski J, Decenciere E, Bidault F (2008) Interactive liver tumor segmentation using graph-cuts and watershed. In: 11th international conference on medical image computing and computer assisted intervention. In Workshop on 3D segmentation in the clinic: a grand challenge II. Liver Tumor Segmentation Challenge. MICCAI, New York, USA, pp 1–12‏

  50. Pan S, Dawant BM (2001) Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach. In: International Society for Optics and Photonics on Medical Imaging, pp 128–138‏

  51. Lim SJ, Jeong YY, Ho YS (2006) Automatic liver segmentation for volume measurement in CT Images. J Vis Commun Image Represent 17(4):860–875

    Article  Google Scholar 

  52. Massoptier L, Casciaro S (2007) Fully automatic liver segmentation through graph-cut technique. In: 29th annual international conference of the IEEE on engineering in medicine and biology society (EMBS 2007), pp 5243–5246

  53. Li G, Chen X, Shi F, Zhu W, Tian J, Xiang D (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT images. IEEE Trans Image Process 24(12):5315–5329

    Article  MathSciNet  MATH  Google Scholar 

  54. Ali AR, Couceiro MS, Anter AM, Hassanian AE (2014) Evaluating an evolutionary particle swarm optimization for fast fuzzy c-means clustering on liver ct images. IGI, Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, pp 1–21‏

  55. Massoptier L, Casciaro S (2008) A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol 18(8):1658–1665

    Article  Google Scholar 

  56. Ben-Cohen A, Klang E, Diamant I, Rozendorn N, Amitai MM, Greenspan H (2015) Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations. J Med Imaging 2(3):034502

    Article  Google Scholar 

  57. Alahmer H, Ahmed A (2015) Computer-aided classification of liver lesions using contrasting features difference. In: ICMISC 2015: 17th international conference on medical image and signal computing, pp 27–28 November 2015, London

  58. Lu YC, Kemper AR, Gayzik S, Untaroiu CD, Beillas P (2013) Statistical modeling of human liver incorporating the variations in shape, size, and material properties. Stapp Car Crash J 57:285–311

    Google Scholar 

  59. Lu YC (2014) Probabilistic analysis of the material and shape properties for human liver (Doctoral dissertation, Virginia Tech)

  60. Platero C, Tobar MC (2014) A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans. Comput Math Methods Med

  61. Linguraru MG, Sandberg JK, Li Z, Shah F, Summers RM (2010) Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Med Phys 37(2):771–783

    Article  Google Scholar 

  62. Cheng HD, Guo Y, Zhang Y (2011) A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm. New Math Nat Comput 7(01):155–171

    Article  MATH  Google Scholar 

  63. Anter AM, Hassanien AE, ElSoud MAA, Tolba MF (2014) Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation. In: Proceedings of the fifth international conference on innovations in bio-inspired computing and applications (IBICA2014), Springer, New York, pp 193–203‏

  64. Zhang M (2010) Novel approaches to image segmentation based on neutrosophic logic, PhD, Utah State University. http://digitalcommons.usu.edu/etd/795/, July, 2014

  65. Siri SK, Latte MV (2017) A novel approach to extract exact liver image boundary from abdominal CT scan using neutrosophic set and fast marching method. J Intell Syst. https://doi.org/10.1515/jisys-2017-0144

    Article  Google Scholar 

  66. Siri SK, Latte MV (2017) Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan. Comput Methods Programs Biomed 151:101–109

    Article  Google Scholar 

  67. Anter AM, Bhattacharyya S, Zhang Z (2020) Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans. Appl Soft Comput 96:106677

    Article  Google Scholar 

  68. Venkatesan A, Parthiban L (2013) Hybridized algorithms for medical image segmentation. Int J Eng Adv Technol 2(3):305–307

    Google Scholar 

  69. Mostafa A, Fouad A, Elfattah MA, Hassanien AE, Hefny H, Zhu SY, Schaefer G (2015) CT liver segmentation using artificial bee colony optimisation. Procedia Comput Sci 60:1622–1630

    Article  Google Scholar 

  70. Singh I, Gupta N (2015) Optimized liver segmentation using ant colony optimization. Int J Adv Res Electron Commun Eng 4(9):2434–2439

    Google Scholar 

  71. El-Masry WH, Emary E, Hassanien AE (2014) Automatic liver CT image clustering based on invasive weed optimization algorithm. In: 2014 international conference on engineering and technology (ICET), pp 1–5. IEEE

  72. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

  73. Eapen M, Korah R, Geetha G (2015) Swarm intelligence integrated graph-cut for liver segmentation from 3D-CT volumes. Sci World J

  74. Sayed GI, Hassanien AE (2016) Abdominal CT liver parenchyma segmentation based on particle swarm optimization. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015, BeniSuef, Egypt. Springer, Cham, pp 219–228

  75. Danciu M, Gordan M, Florea C, Vlaicu A (2012) 3D DCT supervised segmentation applied on liver volumes. In: 35th international conference of the IEEE in telecommunications and signal processing (TSP), pp 779–783

  76. Kumar SS, Moni RS, Rajeesh J (2012) Liver tumor diagnosis by gray level and contourlet coefficients texture analysis. In: International conference on IEEE in computing, electronics and electrical technologies (ICCEET2012), pp 557–562

  77. Aldeek N, Alomari R, Al-Zoubi M, Hiary H (2014) Liver segmentation from abdomen Ct images with Bayesian model. J Theoret Appl Inf Technol 60(3):483–490

    Google Scholar 

  78. Susomboon R, Raicu DS, Furst J (2007) A hybrid approach for liver segmentation. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 151–160‏

  79. Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing. In: 1st conference on medical imaging with deep learning (MIDL 2018), Amsterdam, The Netherlands, pp 1–9

  80. Bellver M, Maninis KK, Pont Tuset J, Giró Nieto X, Torres Vinals J, Van Gool L (2017) Detection-aided liver lesion segmentation using deep learning. In: Advances in neural information processing systems 30 (NIPS 2017): NIPS proceedings, pp 1–5

  81. Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Sommer WH (2016) Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 415–423

  82. Han X (2017) Automatic liver lesion segmentation using a deep convolutional neural network method. arXiv preprint arXiv:1704.07239

  83. Amitai MM, Greenspan H (2017) Virtual PET images from CT data using deep convolutional networks: initial results. In: Simulation and synthesis in medical imaging: second international workshop, SASHIMI 2017, held in conjunction with MICCAI 2017, Québec City, QC, Canada, September 10, 2017, Proceedings, vol 10557. Springer, New York, p 49

  84. Kalinovsky A, Liauchuk V, Tarasau A (2017) Lesion detection in ct images using deep learning semantic segmentation technique. Int Arch Photogramm Remote Sens Spatial Inf Sci 42:13

    Article  Google Scholar 

  85. Yasaka K, Akai H, Abe O, Kiryu S (2017) Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 286(3):887–896

    Article  Google Scholar 

  86. Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng PA (2016) 3D deeply supervised network for automatic liver segmentation from CT volumes. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 149–157

  87. Hu P, Wu F, Peng J, Liang P, Kong D (2016) Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 61(24):8676

    Article  Google Scholar 

  88. Roth HR, Lu L, Farag A, Sohn A, Summers RM (2016) Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 451–459

  89. Li W, Jia F, Hu Q (2015) Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J Comput Commun 3(11):146

    Article  Google Scholar 

  90. Vivanti R, Ephrat A, Joskowicz L, Karaaslan OA, Lev-Cohain N, Sosna J (2015) Automatic liver tumor segmentation in follow up CT studies using convolutional neural networks. In: Proc. patch-based methods in medical image processing workshop, vol 2

  91. Anter AM, Hassanien AE, ElSoud MA, Azar AT (2015) Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques. Int J Biomed Eng Technol 17(2):148–167

    Article  Google Scholar 

  92. Ge F, Wang S, Liu T (2007) New benchmark for image segmentation evaluation. J Electron Imaging 16(3):033011

    Article  Google Scholar 

  93. Hussain K, Salleh MNM, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233

    Article  Google Scholar 

  94. Anter AM, Ali M (2020) Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Comput 24(3):1565–1584

    Article  Google Scholar 

  95. Tavanaei A, Ghodrati M, Kheradpisheh SR, Masquelier T, Maida A (2019) Deep learning in spiking neural networks. Neural Netw 111:47–63

    Article  Google Scholar 

  96. Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T (2018) STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw 99:56–67

    Article  Google Scholar 

  97. Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A et al (2018) Generalized leaky integrate-and-fire models classify multiple neuron types. Nat Commun 9(1):709

    Article  Google Scholar 

  98. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  MathSciNet  MATH  Google Scholar 

  99. Pfeiffer M, Pfeil T (2018) Deep learning with spiking neurons: opportunities and challenges. Front Neurosci 12:774–792

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Information Technology (CSIT), Egypt-Japan University of Science and Technology (E-JUST), Alexandria, 21934, Egypt

    Ahmed M. Anter

  2. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef, 62511, Egypt

    Ahmed M. Anter

  3. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  4. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  5. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  6. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  7. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

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AMA: Supervision, Conceptualization, Methodology, Investigation, Validation, Writing- Original draft preparation. LA: Supervision, Conceptualization, Methodology,, Investigation, Writing- Original draft preparation. All authors read and approved the fnal manuscript.

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Anter, A.M., Abualigah, L. Deep Federated Machine Learning-Based Optimization Methods for Liver Tumor Diagnosis: A Review. Arch Computat Methods Eng 30, 3359–3378 (2023). https://doi.org/10.1007/s11831-023-09901-4

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