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Comparative analysis of 5 regions over 13 regions bone age assessment via TW3 method with deep learning

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Abstract

Purpose

The deep learning-aided automatic bone age assessment systems have obtained the best results but still depending on the advanced methods to attain highly accurate region of interest (ROI) segmentation and error-free bone age assessment based on different regions of bone. By avoiding these drawbacks, this paper presents a comparative analysis on a deep learning-aided bone age assessment using 5 regions over 13 regions under Tanner-Whitehouse 3 (TW3) method.

Methods

The segmentation of both 5 regions and 13 regions is employed by both adaptive Otsu thresholding and improved U-Net segmentation. Further, the adoption of enhanced “deep convolutional neural network (CNN) is used for the bone age assessment (BAA)” using the 13 region and 5 regions. The development of Opposition Searched Harris Hawks Optimization (OS-HHO) is preferable for both segmentation and prediction.

Results

From the analysis, the root mean square error (RMSE) of 5th region is 8.45% better than that of the 13th region. Similarly, the mean absolute squared error (MASE) of 5th region was 2.30% higher than the 13th region.

Conclusion

The assessment of bone age by 5 regions was better when compared with the 13 regions for the suggested bone age assessment (BAA) model, respectively.

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References

  • Abbasi MU, Rashad A, Basalamah A, and Tariq M, Detection of epilepsy seizures in neo-natal EEG using LSTM architecture, IEEE Access, 2019; 7.

  • Alcina M, Lucea A, Salicru M, and Turbon D, Reliability of the Greulich & Pyle method for bone age estimation in a Spanish sample, J Forensic Leg Invest Sci. 2015; 1(3).

  • Antonio T, Juan A. A radius and ulna TW3 bone age assessment system. IEEE Trans Biomed Eng. 2008;55(5):1463–76.

    Article  Google Scholar 

  • Bakthula R and Agarwal S, Automated human bone age assessment using image processing methods – survey. Int J Comput Appl. 2014; 104(13).

  • Bojja GR, Ofori M, Liu J, Ambati LS (2020) Early public outlook on the coronavirus disease (COVID-19): a social media study. Social Media Analysis on Coronavirus (COVID-19).

  • Cao F, Huang H, Pietka E, Gilsanz V. Digital hand atlas and web-based bone age assessment: system design and implementation. Computerized Med Imag Graph. 2000;24:297–307.

    Article  Google Scholar 

  • Chemtex RM, Kantheti S. Kantheti S Classification of skin cancer using deep learning, convolutional neural networks -opportunities and vulnerabilities-a systematic review. Int J Modern Trends Sci Technol. 2020;6(11):101–8.

    Article  Google Scholar 

  • Chen X, Li J, Zhang Y, Lu Y, Liu S. Automatic feature extraction in X-ray image based on deep learning approach for determination of bone age. Future Gener Comput Syst. 2020;110:795–801.

    Article  Google Scholar 

  • Chen Y, Yang J, Qian J. Recurrent neural network for facial landmark detection. Neurocomputing. 2017;219:26–38.

    Article  Google Scholar 

  • Christoforidis A, Badouraki M, Katzos G, Athanassiou-Metaxa M. Bone age estimation and prediction of final height in patients with β-thalassaemia major: a comparison between the two most common methods. Pediatric Radiol. 2007;37:1241–6.

    Article  Google Scholar 

  • Deshmukh S and Khaparde A, Faster region-convolutional neural network oriented feature learning with optimal trained recurrent neural network for bone age assessment for pediatrics, Biomed. Signal Process. Control. 2022; 71.

  • Fischer B, Welter P, Gunther RW, Deserno TM. Web-based bone age assessment by content-based image retrieval for case-based reasoning. Int Jof Comput Assisted Radiol and Surgery. 2012;7(3):389–99.

    Article  Google Scholar 

  • Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang H. Bone age assessment of children using a digital hand atlas. Comput Med Imag Graph. 2007;31:322–31.

    Article  Google Scholar 

  • He J and Jiang D, Fully automatic model based on SE-ResNet for bone age assessment, in IEEE Access 2021; 9: 62460–62466.

  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: algorithm and applications. Future Gener Comput Syst. 2019;97:849–72.

    Article  Google Scholar 

  • Hussien AG et al 2020 Crow search algorithm: theory, recent advances, and applications, in IEEE Access, 2020; 8: 173548–173565.

  • Iglovikov V, Rakhlin A, Kalinin A, and Shvets A, Pediatric bone age assessment using deep convolutional neural networks, arXiv:1712.05053 2017.

  • Jagadeeshwar L. Tabjula, Srijith Kanakambaran, Sheetal Kalyani, Prabhu Rajagopal, Balaji Srinivasan " Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring," Structural control and Health Monitering, Volume 28, Issue, March 2021.

  • Jagadeeshwar Tabjula, S. Kalyani, Prabhu Rajagopal, Balaji Srinivasan "Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves," Structural Health Monitoring, 2021.

  • Kim J, Shim W, Yoon H, Hong S, Lee J, Cho Y, Kim S. Computerized bone age estimation using deep learning based program: evaluation of accuracy and efficiency. Am J Roentgenol. 2017;209:1374–80.

    Article  Google Scholar 

  • King D, et al. Reproducibility of bone ages when performed by radiology registrars: an audit of Tanner and Whitehouse II versus Greulich and Pyle methods. The Brit J Radiol. 1994;67:848–51.

    Article  Google Scholar 

  • Kowdiki M, and Khaparde A, Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping, Comput. Sci. Rev. 2021; 39.

  • Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S. Fully automated deep learning system for bone age assessment. J Digit Imaging. 2017;30:427–41.

    Article  Google Scholar 

  • Li K. et al., Automatic bone age assessment of adolescents based on weakly-supervised deep convolutional neural networks, in IEEE Access, 2021; 9: 120078–120087.

  • Liu J, Qi J, Liu Z, Ning Q, Luo X. Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method. Comput Med Imag Graph. 2008;32:678–84.

    Article  Google Scholar 

  • Liu Y, Zhang C, Cheng J, Chen X, Wang ZJ. A multi-scale data fusion framework for bone age assessment with convolutional neural networks. Comput Biol Med. 2019;108:161–73.

    Article  Google Scholar 

  • Malipatil S, Maheshwari V and Chandra MB, Area optimization of CMOS full adder design using 3T XOR, Int. Conf.Wireless Commun. Signal Processing and Networking (WiSPNET), 2020; 192–194.

  • Mansourvar M, Ismail MA, Herawan T, Raj RG, Kareem SA, and Nasaruddin FH, Automated bone age assessment: motivation, taxonomies, and challenges, Comput. Math. Methods Med. 2013; 2013.

  • Markus H, Benedikt F, Hauke S, Thomas S. Support vector machine classification based on correlation prototypes applied to bone age assessment. IEEE J Biomed Health Inf. 2013;17(1):190–7.

    Article  Google Scholar 

  • Merzban MH, Elbayoumi M. Efficient solution of Otsu multilevel image thresholding: a comparative study. Expert Syst Appl. 2019;116:299–309.

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Software. 2014;69:46–61.

    Article  Google Scholar 

  • Pan X, Zhao Y, Chen H, Wei D, Zhao C, Wei Z. Fully automated bone age assessment on large-scale hand X-ray dataset. Int J Biomed Imag. 2020;2020:1–12.

    Article  Google Scholar 

  • Pietka E, Gertych A, Pospiech S, Cao F, Huang H, Gilsanz V. Computer-assisted bone age assessment: image preprocessing and epiphyseal/ metaphyseal ROI extraction. IEEE Trans Med Imag. 2001;20(8):715–29.

    Article  Google Scholar 

  • Preetha NSN, Brammya G, Ramya R, and Praveena S, Grey wolf optimization-based feature selection and classification for facial emotion recognition, IET Biom. 2018; 7(5).

  • Ramesh S, Vydeki D, Recognition and classification of paddy leaf diseases using optimized deep neural network with Jaya algorithm, Inf. Process. in Agric. 2019.

  • Ren X, et al. Regression Convolutional neural network for automated pediatric bone age assessment from hand radiograph. IEEE J Biomed Health Inf. 2019;23(5):2030–8.

    Article  Google Scholar 

  • Ronneberger O, Fischer P, and Brox T, U-Net: convolutional networks for biomedical image segmentation, Springer: 2015; 234–241.

  • Son SJ. et al., TW3-based fully automated bone age assessment system using deep neural networks, in IEEE Access. 2019; 7: 33346–33358.

  • Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41–51.

    Article  Google Scholar 

  • Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imag. 2009;28(1):52–66.

    Article  Google Scholar 

  • Wang D, Tan D, Liu L. Particle swarm optimization algorithm: an overview. Soft Comput. 2018;22(2):387–408.

    Article  Google Scholar 

  • Wibisono A & Mursanto P, Multi region-based feature connected layer (RB-FCL) of deep learning models for bone age assessment, J Big Data. 2020; 7.

  • Zarie M, Jahedsaravani A, Massinaei M Flotation froth image classification using convolutional neural networks, Miner Eng. 2020;155.

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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Authors and Affiliations

  1. Department of Electronics and Telecommunications, Maharashtra Institute of Technology, Pune, Maharashtra, India

    Sonal Deshmukh

  2. School of Electronics & Communication Engineering, Dr. Vishwanath Karadmit World Peace University, Pune, Maharashtra, India

    Arti Khaparde

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  1. Sonal Deshmukh
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  2. Arti Khaparde
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Sonal Deshmukh.

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Deshmukh, S., Khaparde, A. Comparative analysis of 5 regions over 13 regions bone age assessment via TW3 method with deep learning. Res. Biomed. Eng. 38, 871–900 (2022). https://doi.org/10.1007/s42600-022-00225-z

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