Abstract
Automated healthcare product classification is a challenging field of research that has recently gained a lot of interest. This process is beneficial in terms of time and cost but problematic in obtaining annotated data and lacks uniformity. The high volume of healthcare products and their categories raise the need for machine learning models that can decrease the time and cost spent by human editors. Deep learning techniques that have recently emerged are applied to automated healthcare data classification. The efficacy of the deep learning model depends on the training data and the learning model's suitability for the data domain. When the dataset is large, training a model requires potent processors, including GPUs, and might take hours. However, when such a large volume of data is unavailable, the Conventional Neural Network (CNN) does not train well for the lack of enough samples. To overcome this issue, an effective classification method is proposed to classify the products with a contemporary architecture that integrates the data selection, transformation, and filtering processes with the training of CNN and long short-term memory (LSTM) with limited labeled data and has an imbalance among the classes. The efficiency of the hybrid LSTM approach is evaluated using ResNet, Google Net, and Alex Net. The models were trained using different hyperparameters and the accuracy of the network trained on this data and the accuracy of AlexNet is 94.38, GoogleNet is 94.82 and ResNet-50 is 95.37. Finally, the proposed approach demonstrates that using an efficient classifier at the end of the CNN structure delivers the desired performance even when the CNN model is not intensely trained.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
References
Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: Proceedings of the 2017 international conference on engineering and technology (ICET), Antalya, Turkey, pp 1–6
Al-Bulushi NI, King PR, Blunt MJ, Kraaijveld M (2012) Artificial neural networks workflow and its application in the petroleum industry. Neural Comput Appl 21(3):409–421
Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166
Boureau YL, Bach F, Lecun Y, Ponce J (2010) Learning mid-level features for recognition. In: Computer vision and pattern recognition, pp 2559–2566
Cevahir A and Murakami K (xxxx) Large-scale multi-class and hierarchical product categorization for an
Chen L, Yang F, Yang H (xxxx) Image-based product recommendation system with convolutional neural networks
Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies, a survey on big data. Inf Sci 275:314–347
Cogan T, Cogan M, Tamil L (2019) MAPGI: accurate identification of anatomical landmarks and disease tissue in the gastrointestinal tract using deep learning. Comput Biol Med 111:103351
Gupta V, Karnick H (2016a) Automatic tagging and retrieval of E-Commerce products based on visualfeatures. In: Proceedings of NAACL-HLT 2016a, San Diego
Bhardwaj A, Iyer S (2016b) Large-scale product classification via text and image-based signals using afusion of discriminative and deep learning-based classifiers. In: O'Reilly Conference, San Jose
Fisher WD, Tracy K, Krzhizhanovskaya VV et al (2017) Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection. J Comput Sci 20:143–153
Fishkin R and Staff M (xxxx) How Search Engines Operate. https://moz.com
Habibzadeh M, Jannesari M, Rezaei Z, Baharvand H, Totonchi M (2018) Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception, SPIE, MV
Hand D, Christen P (2018) A note on using the Fmeasure for evaluating record linkage algorithms. Stat Comput 28(3):539–547
Hinton GE, Osindero C, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780
Hussain M, Bird JJ, Faria DR (xxxx) A study on CNN transfer learning for image classification, Birmingham
Kannan A, Talukdar PP, Rasiwasia N, Ke Q (xxxx) Improving product classification using images
Kim Y (2014) Convolutional Neural Networks for Sentence Classification. arXiv, arXiv:1408.5882
Kirkerød M, Borgli RJ, Thambawita V, Hicks S, Riegler MA, Halvorsen P (2019a) Unsupervised preprocessing to improve generalization for medical image classification, pp 1–6
Kirkerød M, Borgli RJ, Thambawita V, Hicks S, Riegler MA, Halvorsen P (2019b) Unsupervisedpreprocessing to improve generalization for medical image classification, pp 1–6
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60:84–90
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1, Lake Tahoe, Nevada, pp 1097–1105
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324
Ludwig SA, Picek S, Jakobovic D et al (2018) Classification of cancer data: analyzing gene expression data using a fuzzy decision tree algorithm. In: Kahraman C, Topcu Y (eds) Operations research applications in health care management. Springer, Cham, pp 327–347
Mikolov T, Kombrink S, Burget L, Černocký J, Khudanpur S (2011) Extensions of recurrent neural network language model, pp 5528–5531
Morente-Molinera JA, Mezei J, Carlsson C et al (2017) Improving supervised learning classification methods using multi granular linguistic modelling and fuzzy entropy. IEEE Trans Fuzzy Syst 25(5):1078–1089
Nguyen T, Khosravi A, Creighton D et al (2015) Classification of healthcare data using the genetic fuzzy logic system and wavelets. Expert Syst Appl 42(4):2184–2197
O'Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv, arXiv:1511.08458
Pogorelov K, Randel KR, Griwodz C, Eskeland SL, d Lange T, Johansen D, Spampinato C, Dang- Nguyen D-T, Lux M, Schmidt PT, Riegler M, #229, and Halvorsen I (2017) KVASIR: a multi-class image dataset for computer-aided gastrointestinal disease detection. In: Proceedings of the 8th ACM onMultimedia Systems Conference, Taipei, Taiwan, pp 164–169
Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition, pp 512–519
Rokach L (2006) Decomposition methodology for classification tasks: a meta decomposer framework. Pattern Anal Appl 9(2):257–271
Sahare M, Gupta H (2012) A review of multi-class classification for imbalanced data. Int J Adv Comput Res 2(5):160–164
Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-k, Woo W-c (2015) Convolutional LSTM Network: machine learning approach for precipitation nowcasting. In: Proceedings of the 28th international conference on neural information processing systems, vol 1, Montreal, Canada, pp 802–810
Shin H, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics, and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2014 arXiv1409.4842S
Taneja S, Suri B, Gupta S et al (2008) A fuzzy logic-based approach for data classification. Data Eng Intell Comput 5:605–616
Targ S, Almeida D, Lyman K (2016) Resnet in Resnet: generalizing residual architectures. arXiv eprints, https://ui.adsabs.harvard.edu/abs/2016arXiv160308029T
Visa S, Ramsay B, Ralescu AL, Van Der Knaap E (2011) Confusion matrix-based feature selection. In: MAICS, pp 120–127
Wu X, Zhu X, Wu GQ et al (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107
Yi-Min H, Shu-Xin D (2005) Weighted support vector machine for classification with uneven training class sizes, pp 4365–4369
Yu W, Sun Z, Liu H, Li Z, Zheng Z (2018) Multi-level deep learning based e-commerce product categorization. In: eCOM@ SIGIR. Available online: https://sigir-ecom.github.io/ecom2018/accepted-papers.html
Zhai C, Cohen WW, Lafferty J (2015) Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: ACM SIGIR forum, vol 49, no 1, pp 2–9. ACM
Funding
No funding is involved in this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationship that could have appeared to influence the work reported in this paper.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Reddy, B.R., Kumar, R.L. Classification of health care products using hybrid CNN-LSTM model. Soft Comput 27, 9199–9216 (2023). https://doi.org/10.1007/s00500-023-08279-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-08279-6