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In addition, the method can highlight the bone areas inside CT images and transform 2D slices into a visual 3D model to illustrate the structure of human body parts. Firstly, we leveraged shallow convolutional Neural Networks to classify body parts and detect bone areas in each part. Then, Grad-CAM was applied to highlight the bone areas. Finally, Insight and Visualization libraries were utilized to visualize slides in 3D of a body part. As a result, the classifiers achieved 98 % in F1-score in the classification of human body parts on a CT image dataset, including 1234 slides capturing body parts from a woman for the training phase and 1245 images from a male for testing. In addition, distinguishing between bone and non-bone images can reach 97 % in F1-score on the dataset generated by setting a threshold value to reveal bone areas in CT images. Moreover, the Grad-CAM-based approach can provide clear, accurate visualizations with segmented bones in the image. Also, we successfully converted 2D slice images of a body part into a lively 3D model that provided a more intuitive view from any angle. The proposed approach is expected to provide an interesting visual tool for supporting doctors in medical image-based disease diagnosis.<\/jats:p>","DOI":"10.2478\/acss-2023-0007","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T06:44:53Z","timestamp":1692341093000},"page":"66-77","source":"Crossref","is-referenced-by-count":0,"title":["Recognition and 3D Visualization of Human Body Parts and Bone Areas Using CT Images"],"prefix":"10.2478","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1386-1390","authenticated-orcid":false,"given":"Hai Thanh","family":"Nguyen","sequence":"first","affiliation":[{"name":"College of Information and Communication Technology , Can Tho University , Can Tho , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"My N.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Information and Communication Technology , Can Tho University , Can Tho , Vietnam"},{"name":"Kyoto Institute of Technology , Kyoto , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bang Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Information and Communication Technology , Can Tho University , Can Tho , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linh Chi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Information and Communication Technology , Can Tho University , Can Tho , Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linh Duong","family":"Phung","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering , Ritsumeikan University , Kyoto , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"2026042709093833353_j_acss-2023-0007_ref_001","doi-asserted-by":"crossref","unstructured":"X. Wang, J. Yu, Q. Zhu, S. Li, Z. Zhao, B. Yang, and J. Pu, \u201cPotential of deep learning in assessing pneumoconiosis depicted on digital chest radiography,\u201d Occupational and Environmental Medicine, vol. 77, no. 9, pp. 597\u2013602, 2020. https:\/\/doi.org\/10.1136\/oemed-2019-106386","DOI":"10.1136\/oemed-2019-106386"},{"key":"2026042709093833353_j_acss-2023-0007_ref_002","doi-asserted-by":"crossref","unstructured":"H. Tang and Z. Hu, \u201cResearch on medical image classification based on machine learning,\u201d IEEE Access, vol. 8, pp. 93145\u201393154, 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993887","DOI":"10.1109\/ACCESS.2020.2993887"},{"key":"2026042709093833353_j_acss-2023-0007_ref_003","doi-asserted-by":"crossref","unstructured":"A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, \u201cDermatologist-level classification of skin cancer with deep neural networks,\u201d Nature, vol. 542, no. 7639, pp. 115\u2013118, Jan. 2017. https:\/\/doi.org\/10.1038\/nature21056","DOI":"10.1038\/nature21056"},{"key":"2026042709093833353_j_acss-2023-0007_ref_004","doi-asserted-by":"crossref","unstructured":"L. Song, T. Xing, Z. Zhu, W. Han, G. Fan, J. Li, H. Du, W. Song, Z. Jin, and G. Zhang, \u201cHybrid clinical-radiomics model for precisely predicting the invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodule,\u201d Academic Radiology, vol. 28, no. 9, Sep. 2021. https:\/\/doi.org\/10.1016\/j.acra.2020.05.004","DOI":"10.1016\/j.acra.2020.05.004"},{"key":"2026042709093833353_j_acss-2023-0007_ref_005","unstructured":"L. Ibanez, W. Schroeder, L. Ng, and J. Cates, The ITK Software Guide and the Insight Software Consortium: updated for ITK version 2.4. Erscheinungsort nicht ermittelbar: Kitware Inc, 2005. https:\/\/www.igb.illinois.edu\/sites\/default\/files\/upload\/core\/PDF\/ItkSoftwareGuide-2.4.0.pdf"},{"key":"2026042709093833353_j_acss-2023-0007_ref_006","doi-asserted-by":"crossref","unstructured":"R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, \u201cGrad-cam: Visual explanations from deep networks via gradient-based localization,\u201d International Journal of Computer Vision, vol. 128, no. 2, pp. 336\u2013359, Feb. 2020. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","DOI":"10.1007\/s11263-019-01228-7"},{"key":"2026042709093833353_j_acss-2023-0007_ref_007","doi-asserted-by":"crossref","unstructured":"A. A. Giannopoulos and T. Pietila, \u201cPost-processing of DICOM Images,\u201d in 3D Printing in Medicine: A Practical Guide for Medical Profession-als, F. J. Rybicki and G. T. Grant, Eds. Cham: Springer International Publishing, 2017, pp. 23\u201334. https:\/\/doi.org\/10.1007\/978-3-319-61924-8_3","DOI":"10.1007\/978-3-319-61924-8_3"},{"key":"2026042709093833353_j_acss-2023-0007_ref_008","doi-asserted-by":"crossref","unstructured":"C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, \u201cA level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI,\u201d IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, vol. 20, pp. 2007\u20132016, Jul. 2011. https:\/\/doi.org\/10.1109\/TIP.2011.2146190","DOI":"10.1109\/TIP.2011.2146190"},{"key":"2026042709093833353_j_acss-2023-0007_ref_009","doi-asserted-by":"crossref","unstructured":"Y.-L. Ling &. A.-H. Zhong, \u201cResearch on the classification of body type and prototype of middle-aged women based on 3D scanning,\u201d Journal of Fiber Bioengineering and Informatics, vol. 13, no. 3, pp. 161\u2013167, Nov. 2020. https:\/\/doi.org\/10.3993\/jfbim00344","DOI":"10.3993\/jfbim00344"},{"key":"2026042709093833353_j_acss-2023-0007_ref_010","doi-asserted-by":"crossref","unstructured":"C. Hellmann, A. Bajrami, and W. Kraus, \u201cEnhancing a robot gripper with haptic perception for risk mitigation in physical human robot interaction,\u201d in 2019 IEEE World Haptics Conference (WHC), Tokyo, Japan, Jul. 2019, pp. 253\u2013258. https:\/\/doi.org\/10.1109\/whc.2019.8816109","DOI":"10.1109\/WHC.2019.8816109"},{"key":"2026042709093833353_j_acss-2023-0007_ref_011","doi-asserted-by":"crossref","unstructured":"F. Li, \u201cClassification of students\u2019 body shape based on deep neural network,\u201d in Innovative Computing.Lecture Notes in Electrical Engineering, C.T. Yang, Y. Pei, and J.W. Chang, Eds., vol. 675. Springer Singapore, 2020, pp. 549\u2013557. https:\/\/doi.org\/10.1007\/978-981-15-5959-4_66","DOI":"10.1007\/978-981-15-5959-4_66"},{"key":"2026042709093833353_j_acss-2023-0007_ref_012","doi-asserted-by":"crossref","unstructured":"J. F. Yu, L. Pung, H. Minami, K. Mueller, R. Khangura, R. Darflinger, S. W. Hetts, and D. L. Cooke, \u201cVirtual 2D angiography from four-dimensional digital subtraction angiography (4D-DSA): A feasibility study,\u201d Interv. Neuroradiol., vol. 27, no. 2, Sep. 2020. https:\/\/doi.org\/10.1177\/1591019920961604","DOI":"10.1177\/1591019920961604"},{"key":"2026042709093833353_j_acss-2023-0007_ref_013","doi-asserted-by":"crossref","unstructured":"M. Boussif, N. Aloui, and A. Cherif, \u201cDICOM imaging watermarking for hiding medical reports,\u201d Medical and Biological Engineering and Computing, vol. 58, no. 11, pp. 2905\u20132918, Sep. 2020. https:\/\/doi.org\/10.1007\/s11517-020-02269-8","DOI":"10.1007\/s11517-020-02269-8"},{"key":"2026042709093833353_j_acss-2023-0007_ref_014","doi-asserted-by":"crossref","unstructured":"X. Jiang, Y. Zhang, Q. Yang, B. Deng, and H. Wang, \u201cMillimeter-wave array radar-based human gait recognition using multi-channel three-dimensional convolutional neural network,\u201d Sensors, vol. 20, no. 19, Sep. 2020, Art. no. 5466. https:\/\/doi.org\/10.3390\/s20195466","DOI":"10.3390\/s20195466"},{"key":"2026042709093833353_j_acss-2023-0007_ref_015","doi-asserted-by":"crossref","unstructured":"N. S. Chan, K. I. Chan, R. Tse, S.-K. Tang, and G. Pau, \u201cReSPEcT: privacy respecting thermal-based specific person recognition,\u201d in Thirteenth International Conference on Digital Image Processing (ICDIP 2021), X. Jiang and H. 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Nie, \u201cSafety helmet detection based on YOLOv5,\u201d in 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China, Jan. 2021, pp. 6\u201311. https:\/\/doi.org\/10.1109\/ICPECA51329.2021.9362711","DOI":"10.1109\/ICPECA51329.2021.9362711"},{"key":"2026042709093833353_j_acss-2023-0007_ref_018","doi-asserted-by":"crossref","unstructured":"A. Ashraf, T. S. Gunawan, F. D. A. Rahman, and M. Kartiwi, \u201cA summarization of image and video databases for emotion recognition,\u201d in Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, A.F. Ab. Nasir et al., Eds., vol. 730. Springer, Singapore, Jul. 2021, pp. 669\u2013680. https:\/\/doi.org\/10.1007\/978-981-33-4597-3_60","DOI":"10.1007\/978-981-33-4597-3_60"},{"key":"2026042709093833353_j_acss-2023-0007_ref_019","doi-asserted-by":"crossref","unstructured":"D. A. Clunie, \u201cDICOM format and protocol standardization \u2013 a core requirement for digital pathology success,\u201d Toxicologic Pathology, vol. 49, no. 4, Oct. 2020. https:\/\/doi.org\/10.1177\/0192623320965893","DOI":"10.1177\/0192623320965893"},{"key":"2026042709093833353_j_acss-2023-0007_ref_020","doi-asserted-by":"crossref","unstructured":"G. Kwon, J. Ryu, J. Oh, J. Lim, B.-k. Kang, C. Ahn, J. Bae, and D. K. Lee, \u201cDeep learning algorithms for detecting and visualising intussusception on plain abdominal radiography in children: a retrospective multicenter study,\u201d Scientific Reports, vol. 10, no. 1, Oct. 2020, Art. no. 17582. https:\/\/doi.org\/10.1038\/s41598-020-74653-1","DOI":"10.1038\/s41598-020-74653-1"},{"key":"2026042709093833353_j_acss-2023-0007_ref_021","doi-asserted-by":"crossref","unstructured":"I. Lavdas, B. Glocker, D. Rueckert, S. Taylor, E. Aboagye, and A. Rockall, \u201cMachine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data,\u201d Clinical Radiology, vol. 74, no. 5, pp. 346\u2013356, Feb. 2019. https:\/\/doi.org\/10.1016\/j.crad.2019.01.012","DOI":"10.1016\/j.crad.2019.01.012"},{"key":"2026042709093833353_j_acss-2023-0007_ref_022","doi-asserted-by":"crossref","unstructured":"G. Li, X. Shen, J. Li, and J. Wang, \u201cDiagonal-kernel convolutional neural networks for image classification,\u201d Digital Signal Processing, vol. 108, Jan. 2021, Art. no. 102898. https:\/\/doi.org\/10.1016\/j.dsp.2020.102898","DOI":"10.1016\/j.dsp.2020.102898"},{"key":"2026042709093833353_j_acss-2023-0007_ref_023","unstructured":"H. Barzekar and Z. Yu, \u201cC-Net: A reliable convolutional neural network for biomedical image classification,\u201d arXiv preprint, arXiv:2011.00081, 2020. https:\/\/arxiv.org\/pdf\/2011.00081.pdf"},{"key":"2026042709093833353_j_acss-2023-0007_ref_024","doi-asserted-by":"crossref","unstructured":"G. Jia, X. Huang, S. Tao, X. Zhang, Y. Zhao, H. Wang, J. He, J. Hao, B. Liu, J. Zhou, T. Li, X. Zhang, and J. Gao, \u201cArtificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization,\u201d Intelligent Medicine, vol. 2, no. 1, pp. 48\u201352, Feb. 2022. https:\/\/doi.org\/10.1016\/j.imed.2021.04.001","DOI":"10.1016\/j.imed.2021.04.001"},{"key":"2026042709093833353_j_acss-2023-0007_ref_025","unstructured":"Q. Duan, G. Wang, R. Wang, C. Fu, X. Li, M. Gong, X. Liu, Q. Xia, X. Huang, Z. Hu, N. Huang, and S. Zhang, \u201cSenseCare: A research platform for medical image informatics and interactive 3D visualization,\u201d ArXiv, vol. abs\/2004.07031, 2020. arxiv.org\/pdf\/2004.07031.pdf"},{"key":"2026042709093833353_j_acss-2023-0007_ref_026","doi-asserted-by":"crossref","unstructured":"L. Cai, T. Long, Y. Dai, and Y. Huang, \u201cMask R-CNN-based detection and segmentation for pulmonary nodule 3d visualization diagnosis,\u201d IEEE Access, vol. 8, pp. 44400\u201344409, Feb. 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.2976432","DOI":"10.1109\/ACCESS.2020.2976432"},{"key":"2026042709093833353_j_acss-2023-0007_ref_027","doi-asserted-by":"crossref","unstructured":"S. AlZu\u2019bi, M. Shehab, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, \u201cParallel implementation for 3D medical volume fuzzy segmentation,\u201d Pattern Recognition Letters, vol. 130, pp. 312\u2013318, Feb. 2020. https:\/\/doi.org\/10.1016\/j.patrec.2018.07.026","DOI":"10.1016\/j.patrec.2018.07.026"},{"key":"2026042709093833353_j_acss-2023-0007_ref_028","doi-asserted-by":"crossref","unstructured":"D. Mitsouras, P. C. Liacouras, N. Wake, and F. J. Rybicki, \u201cRadioGraphics update: Medical 3D printing for the radiologist,\u201d RadioGraphics, vol. 40, no. 4, pp. E21\u2013E23, Jul. 2020. https:\/\/doi.org\/10.1148\/rg.2020190217","DOI":"10.1148\/rg.2020190217"},{"key":"2026042709093833353_j_acss-2023-0007_ref_029","doi-asserted-by":"crossref","unstructured":"M. Javaid and A. Haleem, \u201cVirtual reality applications toward medical field,\u201d Clinical Epidemiology and Global Health, vol. 8, no. 2, pp. 600\u2013605, Jun. 2020. https:\/\/doi.org\/10.1016\/j.cegh.2019.12.010","DOI":"10.1016\/j.cegh.2019.12.010"},{"key":"2026042709093833353_j_acss-2023-0007_ref_030","doi-asserted-by":"crossref","unstructured":"X. Zhou, T. Hara, H. Fujita, Y. Ida, K. Katada, and K. Matsumoto, \u201cExtraction and recognition of the thoracic organs based on 3D CT images and its application,\u201d in CARS 2002 Computer Assisted Radiology and Surgery, H.U. Lemke et al., Eds. Springer, Berlin, Heidelberg, 2002, pp. 776\u2013781. https:\/\/doi.org\/10.1007\/978-3-642-56168-9_130","DOI":"10.1007\/978-3-642-56168-9_130"},{"key":"2026042709093833353_j_acss-2023-0007_ref_031","unstructured":"M. S. M. Rahim, A. Norouzi, A. Rehman, and T. Saba, \u201c3D bones segmentation based on CT images visualization,\u201d Biomedical Research, vol. 28, no. 8, pp. 3641\u20133644, 2017. https:\/\/www.researchgate.net\/publication\/317745097_3D_bones_segmentation_based_on_CT_images_visualization"},{"key":"2026042709093833353_j_acss-2023-0007_ref_032","doi-asserted-by":"crossref","unstructured":"M. Ackerman, \u201cThe visible human project,\u201d Proceedings of the IEEE, vol. 86, no. 3, pp. 504\u2013511, Mar. 1998. https:\/\/doi.org\/10.1109\/5.662875","DOI":"10.1109\/5.662875"},{"key":"2026042709093833353_j_acss-2023-0007_ref_033","doi-asserted-by":"crossref","unstructured":"L. Friedli, D. Kloukos, G. Kanavakis, D. Halazonetis, and N. Gkantidis, \u201cThe effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images,\u201d Scientific Reports, vol. 10, no. 1, Apr. 2020. https:\/\/doi.org\/10.1038\/s41598-020-64383-9","DOI":"10.1038\/s41598-020-64383-9"},{"key":"2026042709093833353_j_acss-2023-0007_ref_034","doi-asserted-by":"crossref","unstructured":"R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, \u201cGrad-Cam: Visual explanations from deep networks via gradient-based localization,\u201d International Journal of Computer Vision, vol. 128, no. 2, p. 336\u2013359, Oct. 2019. http:\/\/doi.org\/10.1007\/s11263-019-01228-7","DOI":"10.1007\/s11263-019-01228-7"},{"key":"2026042709093833353_j_acss-2023-0007_ref_035","doi-asserted-by":"crossref","unstructured":"H. Panwar, P. Gupta, M. K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, and V. Singh, \u201cA deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images,\u201d Chaos, Solitons & Fractals, vol. 140, Nov. 2020, Art. no. 110190. https:\/\/doi.org\/10.1016\/j.chaos.2020.110190","DOI":"10.1016\/j.chaos.2020.110190"},{"key":"2026042709093833353_j_acss-2023-0007_ref_036","doi-asserted-by":"crossref","unstructured":"R. Fu, Q. Hu, X. Dong, Y. Guo, Y. Gao, and B. Li, \u201cAxiom-based Grad-CAM: Towards accurate visualization and explanation of CNNs,\u201d arXiv, vol. 2008.02312, 2020. https:\/\/arxiv.org\/pdf\/2008.02312.pdf","DOI":"10.5244\/C.34.146"},{"key":"2026042709093833353_j_acss-2023-0007_ref_037","doi-asserted-by":"crossref","unstructured":"P. Morbidelli, D. Carrera, B. Rossi, P. Fragneto, and G. Boracchi, \u201cAugmented Grad-CAM: Heat-maps super resolution through augmentation,\u201d in ICASSP 2020 \u2013 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 2020, pp. 4067\u20134071. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9054416","DOI":"10.1109\/ICASSP40776.2020.9054416"},{"key":"2026042709093833353_j_acss-2023-0007_ref_038","doi-asserted-by":"crossref","unstructured":"H. Jiang, J. Xu, R. Shi, K. Yang, D. Zhang, M. Gao, H. Ma, and W. Qian, \u201cA multi-label deep learning model with interpretable Grad-CAM for diabetic retinopathy classification,\u201d in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, Jul. 2020, pp. 1560\u20131563. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175884","DOI":"10.1109\/EMBC44109.2020.9175884"},{"key":"2026042709093833353_j_acss-2023-0007_ref_039","doi-asserted-by":"crossref","unstructured":"Y. Zhang, D. Hong, D. McClement, O. Oladosu, G. Pridham, and G. Slaney, \u201cGrad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging,\u201d Journal of Neuroscience Methods, vol. 353, Apr. 2021, Art. no. 109098. https:\/\/doi.org\/10.1016\/j.jneumeth.2021.109098","DOI":"10.1016\/j.jneumeth.2021.109098"},{"key":"2026042709093833353_j_acss-2023-0007_ref_040","unstructured":"J. Choi, J. H. Choi, and W. Rhee, \u201cInterpreting neural ranking models using Grad-CAM,\u201d ArXiv, vol. abs\/2005.05768, 2020. arxiv.org\/pdf\/2005.05768.pdf"},{"key":"2026042709093833353_j_acss-2023-0007_ref_041","doi-asserted-by":"crossref","unstructured":"J. Xu, Z. Li, B. Du, M. Zhang, and J. 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