{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:56:36Z","timestamp":1771523796541,"version":"3.50.1"},"reference-count":68,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Indonesian Ministry of Research, Technology and Higher Education.","award":["9\/E1\/KPT\/2020"],"award-info":[{"award-number":["9\/E1\/KPT\/2020"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient\u2019s lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient\u2019s set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3\/L4, L4\/L5, and L5\/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.<\/jats:p>","DOI":"10.1371\/journal.pone.0261659","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T19:27:59Z","timestamp":1642102079000},"page":"e0261659","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier"],"prefix":"10.1371","volume":"17","author":[{"given":"Friska","family":"Natalia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio Christian","family":"Young","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nunik","family":"Afriliana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hira","family":"Meidia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reyhan Eddy","family":"Yunus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-0810","authenticated-orcid":true,"given":"Sud","family":"Sudirman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"issue":"5","key":"pone.0261659.ref001","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3390\/diagnostics11050811","article-title":"Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization","volume":"11","author":"MA Khan","year":"2021","journal-title":"Diagnostics"},{"issue":"1","key":"pone.0261659.ref002","first-page":"1041","article-title":"Computer Decision Support System for Skin Cancer Localization and Classification","volume":"68","author":"MA Khan","year":"2021","journal-title":"C Mater Contin"},{"issue":"8","key":"pone.0261659.ref003","doi-asserted-by":"crossref","first-page":"565","DOI":"10.3390\/diagnostics10080565","article-title":"Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists","volume":"10","author":"MA Khan","year":"2020","journal-title":"Diagnostics"},{"issue":"2","key":"pone.0261659.ref004","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s40815-016-0247-0","article-title":"Spatial Possibilistic Fuzzy C-Mean Segmentation Algorithm Integrated with Brain Mid-sagittal Surface Information","volume":"19","author":"SH Davarpanah","year":"2017","journal-title":"Int J Fuzzy Syst"},{"key":"pone.0261659.ref005","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/978-3-319-12508-4_7","volume-title":"Spinal Imaging and Image Analysis","author":"RS Alomari","year":"2015"},{"issue":"7","key":"pone.0261659.ref006","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.compmedimag.2014.03.005","article-title":"Supervised methods for detection and segmentation of tissues in clinical lumbar MRI","volume":"38","author":"S Ghosh","year":"2014","journal-title":"Comput Med Imaging Graph"},{"issue":"11","key":"pone.0261659.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0241309","article-title":"Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis","volume":"15","author":"F Natalia","year":"2020","journal-title":"PLoS One"},{"issue":"1","key":"pone.0261659.ref008","doi-asserted-by":"crossref","first-page":"e0191442","DOI":"10.1371\/journal.pone.0191442","article-title":"Quantitative MRI in early intervertebral disc degeneration: T1rho correlates better than T2 and ADC with biomechanics, histology and matrix content","volume":"13","author":"CPL Paul","year":"2018","journal-title":"PLoS One"},{"key":"pone.0261659.ref009","doi-asserted-by":"crossref","first-page":"43487","DOI":"10.1109\/ACCESS.2019.2908002","article-title":"Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks","volume":"7","author":"AS Al-Kafri","year":"2019","journal-title":"IEEE Access"},{"key":"pone.0261659.ref010","doi-asserted-by":"crossref","unstructured":"Zhang Q, Bhalerao A, Hutchinson C. Weakly-supervised evidence pinpointing and description. In: International Conference on Information Processing in Medical Imaging. 2017. p. 210\u201322.","DOI":"10.1007\/978-3-319-59050-9_17"},{"issue":"6","key":"pone.0261659.ref011","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/42.650892","article-title":"Automatic detection of the mid-sagittal plane in 3-D brain images","volume":"16","author":"BA Ardekani","year":"1997","journal-title":"IEEE Trans Med Imaging"},{"key":"pone.0261659.ref012","doi-asserted-by":"crossref","DOI":"10.12688\/f1000research.16082.1","article-title":"Current concepts and recent advances in understanding and managing lumbar spine stenosis","volume":"8","author":"C Bagley","year":"2019","journal-title":"F1000Research"},{"key":"pone.0261659.ref013","article-title":"Diagnosis of disc herniation based on classifiers and features generated from spine MR images","volume":"7624","author":"J Koh","year":"2010","journal-title":"Spie Med Imaging Comput Aided Diagnosis"},{"key":"pone.0261659.ref014","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/978-3-319-07269-2_8","volume-title":"Computational Methods and Clinical Applications for Spine Imaging","author":"RS Alomari","year":"2014"},{"key":"pone.0261659.ref015","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.media.2016.10.009","article-title":"Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation","volume":"36","author":"X He","year":"2017","journal-title":"Med Image Anal"},{"issue":"4","key":"pone.0261659.ref016","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.spinee.2019.11.010","article-title":"Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images","volume":"20","author":"J Huang","year":"2020","journal-title":"Spine J"},{"issue":"3","key":"pone.0261659.ref017","article-title":"Radiologic evaluation of lumbar spinal stenosis: the integration of sagittal and axial views in decision making for minimally invasive surgical procedures","volume":"11","author":"J Hartman","year":"2019","journal-title":"Cureus"},{"key":"pone.0261659.ref018","doi-asserted-by":"crossref","unstructured":"Natalia F, Meidia H, Afriliana N, Al-Kafri AS, Sudirman S, Simpson A, et al. Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation. In: IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems. 2018. p. 1449\u201354.","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00239"},{"issue":"2","key":"pone.0261659.ref019","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/TIP.2006.888348","article-title":"Flexible skew-symmetric shape model for shape representation, classification, and sampling","volume":"16","author":"SH Baloch","year":"2007","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"pone.0261659.ref020","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TMI.2013.2241448","article-title":"Feature-based image patch approximation for lung tissue classification","volume":"32","author":"Y Song","year":"2013","journal-title":"IEEE Trans Med Imaging"},{"key":"pone.0261659.ref021","unstructured":"Koitka S, Friedrich CM. Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016. In: CLEF (Working Notes). 2016. p. 304\u201317."},{"key":"pone.0261659.ref022","unstructured":"LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, et al. Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems. 1990. p. 396\u2013404."},{"issue":"4","key":"pone.0261659.ref023","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"Y LeCun","year":"1989","journal-title":"Neural Comput"},{"issue":"8","key":"pone.0261659.ref024","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"S Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"pone.0261659.ref025","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"pone.0261659.ref026","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"V Badrinarayanan","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0261659.ref027","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 580\u20137.","DOI":"10.1109\/CVPR.2014.81"},{"key":"pone.0261659.ref028","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 2961\u20139.","DOI":"10.1109\/ICCV.2017.322"},{"key":"pone.0261659.ref029","doi-asserted-by":"crossref","unstructured":"Kornblith S, Shlens J, Le Q V. Do better imagenet models transfer better? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019. p. 2661\u201371.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"pone.0261659.ref030","doi-asserted-by":"crossref","first-page":"104115","DOI":"10.1016\/j.compbiomed.2020.104115","article-title":"A scoping review of transfer learning research on medical image analysis using ImageNet","volume":"128","author":"MA Morid","year":"2020","journal-title":"Comput Biol Med"},{"key":"pone.0261659.ref031","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009 CVPR 2009 IEEE Conference on. 2009. p. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"pone.0261659.ref032","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.patrec.2020.12.015","article-title":"Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework","volume":"143","author":"MA Khan","year":"2021","journal-title":"Pattern Recognit Lett"},{"key":"pone.0261659.ref033","first-page":"1","article-title":"A deep neural network and classical features based scheme for objects recognition: an application for machine inspection","author":"N Hussain","year":"2020","journal-title":"Multimed Tools Appl"},{"key":"pone.0261659.ref034","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.media.2019.02.010","article-title":"Medical image classification using synergic deep learning","volume":"54","author":"J Zhang","year":"2019","journal-title":"Med Image Anal"},{"key":"pone.0261659.ref035","unstructured":"Sudirman S, Kafri A Al, Natalia F, Meidia H, Afriliana N, Al-Rashdan W, et al. Lumbar Spine MRI Dataset [Internet]. Mendeley Data. 2019 [cited 2019 May 13]. https:\/\/data.mendeley.com\/datasets\/k57fr854j2\/2"},{"issue":"1","key":"pone.0261659.ref036","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/TMI.2003.809072","article-title":"PET-CT image registration in the chest using free-form deformations","volume":"22","author":"D Mattes","year":"2003","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"pone.0261659.ref037","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/42.845174","article-title":"Parametric estimate of intensity inhomogeneities applied to MRI","volume":"19","author":"M Styner","year":"2000","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"pone.0261659.ref038","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/72.761722","article-title":"Fast and robust fixed-point algorithms for independent component analysis","volume":"10","author":"A Hyvarinen","year":"1999","journal-title":"IEEE Trans Neural Networks"},{"key":"pone.0261659.ref039","unstructured":"Fordellone M. Statistical Analysis of Complex Data: Dimensionality reduction and classification methods. 1st. LAP LAMBERT Academic Publishing; 2019."},{"issue":"1","key":"pone.0261659.ref040","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4304\/jcp.7.1.161-168","article-title":"Neighborhood component feature selection for high-dimensional data","volume":"7","author":"W Yang","year":"2012","journal-title":"J Comput"},{"issue":"02","key":"pone.0261659.ref041","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"C Ding","year":"2005","journal-title":"J Bioinform Comput Biol"},{"key":"pone.0261659.ref042","doi-asserted-by":"crossref","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N. A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO). 2015. p. 1200\u20135.","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"pone.0261659.ref043","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. 2012. p. 1097\u2013105."},{"key":"pone.0261659.ref044","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 4700\u20138.","DOI":"10.1109\/CVPR.2017.243"},{"key":"pone.0261659.ref045","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv Prepr arXiv160207261. 2016.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"pone.0261659.ref046","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 2818\u201326.","DOI":"10.1109\/CVPR.2016.308"},{"key":"pone.0261659.ref047","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 4510\u201320.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"pone.0261659.ref048","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0261659.ref049","unstructured":"Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In: International Conference on Learning Representations. 2015."},{"key":"pone.0261659.ref050","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 1251\u20138.","DOI":"10.1109\/CVPR.2017.195"},{"issue":"3","key":"pone.0261659.ref051","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TSMCC.2011.2157494","article-title":"A survey of evolutionary algorithms for decision-tree induction","volume":"42","author":"RC Barros","year":"2011","journal-title":"IEEE Trans Syst Man, Cybern Part C (Applications Rev"},{"key":"pone.0261659.ref052","unstructured":"Vapnik V. The nature of statistical learning theory. Springer science & business media; 2013."},{"issue":"8","key":"pone.0261659.ref053","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"TK Ho","year":"1998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0261659.ref054","unstructured":"Anthony G, Gregg H, Tshilidzi M. Image classification using SVMs: one-against-one vs one-against-all. arXiv Prepr arXiv07112914. 2007."},{"issue":"2","key":"pone.0261659.ref055","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1111\/j.2006.0030-1299.14714.x","article-title":"Entropy and diversity","volume":"113","author":"L Jost","year":"2006","journal-title":"Oikos"},{"issue":"2","key":"pone.0261659.ref056","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"L. Breiman","year":"1996","journal-title":"Mach Learn"},{"issue":"1","key":"pone.0261659.ref057","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Y Freund","year":"1997","journal-title":"J Comput Syst Sci"},{"issue":"1","key":"pone.0261659.ref058","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","article-title":"RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Trans Syst Man","volume":"40","author":"C Seiffert","year":"2010","journal-title":"Cybern Part A Syst Humans"},{"key":"pone.0261659.ref059","unstructured":"Ruder S. An overview of gradient descent optimization algorithms. CoRR [Internet]. 2016;abs\/1609.0. http:\/\/arxiv.org\/abs\/1609.04747"},{"key":"pone.0261659.ref060","doi-asserted-by":"crossref","unstructured":"Zou F, Shen L, Jie Z, Zhang W, Liu W. A sufficient condition for convergences of adam and rmsprop. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019. p. 11127\u201335.","DOI":"10.1109\/CVPR.2019.01138"},{"key":"pone.0261659.ref061","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv Prepr arXiv14126980. 2014."},{"issue":"4","key":"pone.0261659.ref062","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1023\/A:1008306431147","article-title":"Efficient global optimization of expensive black-box functions","volume":"13","author":"DR Jones","year":"1998","journal-title":"J Glob Optim"},{"key":"pone.0261659.ref063","unstructured":"No\u00e8 U, Husmeier D. On a new improvement-based acquisition function for bayesian optimization. arXiv Prepr arXiv180806918. 2018."},{"issue":"10","key":"pone.0261659.ref064","article-title":"Convergence rates of efficient global optimization algorithms","volume":"12","author":"AD Bull","year":"2011","journal-title":"J Mach Learn Res"},{"key":"pone.0261659.ref065","article-title":"Dimension reduction of image deep feature using PCA","volume":"63","author":"J Ma","year":"2019","journal-title":"J Vis Commun Image Represent"},{"key":"pone.0261659.ref066","unstructured":"Sudirman S, Natalia F, Young JC. Algorithm and Dataset for Selecting Mid-Height IVD Slice in Traverse Lumbar Spine MRI [Internet]. Mendeley Data. 2021 [cited 2021 Dec 13]. https:\/\/data.mendeley.com\/datasets\/ggjtzh452d\/1"},{"key":"pone.0261659.ref067","unstructured":"Chollet F. Keras [Internet]. 2015 [cited 2021 Jun 28]. https:\/\/keras.io\/"},{"key":"pone.0261659.ref068","unstructured":"Cournapeau D. Scikit-Learn [Internet]. 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