An image compression approach for efficient pneumonia recognition

References

• WHO (WHO). Pneumonia in children; 2022 [Online; cited 2023 Feb 11]. Available from: https://www.who.int/news-room/fact-sheets/detail/pneumonia.
   Google Scholar
• Aljondi R, Alghamdi S. Diagnostic value of imaging modalities for COVID-19: scoping review. J Med Internet Res. 2020;22(8):e19673.
   PubMed Web of Science ®Google Scholar
• Chowdhury ME, Rahman T, Khandakar A, et al. Can ai help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665–132676.
   Web of Science ®Google Scholar
• Yang Y, Mei G. Pneumonia recognition by deep learning: a comparative investigation. Appl Sci. 2022;12(9):4334.
   Google Scholar
• Eldem H, Ülker E, Işıklı OY. Encoder–decoder semantic segmentation models for pressure wound images. Imaging Sci J. 2022;70(0):75–86. doi:10.1080/13682199.2022.2163531.
   Google Scholar
• Usman M, Khan S, Lee J-A. AFP-LSE: Antifreeze proteins prediction using latent space encoding of composition of k-spaced amino acid pairs. Sci Rep. 2020;10(1):1–13.
   PubMed Web of Science ®Google Scholar
• Latif S, Rana R, Qadir J, et al. Variational autoencoders for learning latent representations of speech emotion: a preliminary study; 2017. arXiv preprint arXiv:1712.08708.
   Google Scholar
• Ayma V, Ayma V, Gutierrez J. Dimensionality reduction via an orthogonal autoencoder approach for hyperspectral image classification. Int Arch Photogramm Remote Sens Spat Inf Sci. 2020;43:357–362.
   Google Scholar
• Menilk S. A branching convolutional encoder-based spatio-spectral dimensionality reduction model for hyperspectral image classification and change detection [PhD thesis]. ASTU; 2021.
   Google Scholar
• Yu W, Zhang M, Shen Y. Spatial revising variational autoencoder-based feature extraction method for hyperspectral images. IEEE Trans Geosci Remote Sens. 2021;59(2):1410–1423. doi:10.1109/TGRS.2020.2997835.
   Web of Science ®Google Scholar
• Oluwasanmi A, Aftab MU, Baagyere E, et al. Attention autoencoder for generative latent representational learning in anomaly detection. Sensors. 2022;22(1):123.
   Web of Science ®Google Scholar
• Yousefi B, Kawakita S, Amini A, et al. Impartially validated multiple deep-chain models to detect COVID-19 in chest X-ray using latent space radiomics. J Clin Med. 2021;10(14):3100.
   PubMed Web of Science ®Google Scholar
• Gunduz H. An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification. Biomed Signal Process Control. 2021;66:102452.
   Web of Science ®Google Scholar
• Feng Y, Zhang L, Yi Z. Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int J Comput Assist Radiol Surg. 2018;13(2):179–191.
   PubMed Web of Science ®Google Scholar
• Palazzo M, Beauseroy P, Yankilevich P. A pan-cancer somatic mutation embedding using autoencoders. BMC Bioinform. 2019;20(1):1–10.
   PubMed Web of Science ®Google Scholar
• Zhang M, Zhang F, Zhang J, et al. Autoencoder for neuroimage. In: Christine S, Gabriele K, Min TA, et al., editors. International conference on database and expert systems applications. Cham: Springer; 2021. p. 84–90.
   Google Scholar
• Tarsitano F, Bruderer C, Schawinski K, et al. Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning; 2021. arXiv preprint arXiv:2105.01070.
   Google Scholar
• Dua M, Makhija D, Manasa P, et al. A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection. J Med Biol Eng. 2020;40(5):688–706.
   Web of Science ®Google Scholar
• Xu X, Zhou F, Liu B. Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN. Int J Comput Assist Radiol Surg. 2018;13(7):967–975.
   PubMed Web of Science ®Google Scholar
• Zheng R, Wang Q, Lv S, et al. Automatic liver tumor segmentation on dynamic contrast enhanced MRI using 4D information: deep learning model based on 3D convolution and convolutional LSTM. IEEE Trans Med Imaging. 2022;41(10):2965–2976.
   PubMed Web of Science ®Google Scholar
• Pustokhin DA, Pustokhina IV, Dinh PN, et al. An effective deep residual network based class attention layer with bidirectional lstm for diagnosis and classification of COVID-19. J Appl Stat. 2020;50(3):477–494. DOI:10.1080/02664763.2020.1849057.
   PubMed Web of Science ®Google Scholar
• Yao H, Zhang X, Zhou X, et al. Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers (Basel). 2019;11(12):1901.
   PubMed Web of Science ®Google Scholar
• Goyal VK. Theoretical foundations of transform coding. IEEE Signal Process Mag. 2001;18(5):9–21.
   Web of Science ®Google Scholar
• Mallick PK, Ryu SH, Satapathy SK, et al. Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access. 2019;7:46278–46287.
   Web of Science ®Google Scholar
• Balĺe J, Laparra V, Simoncelli EP. End-to-end optimization of nonlinear transform codes for perceptual quality; 2016. arXiv:1607.05006.
   Google Scholar
• Balĺe J, Minnen D, Singh S, et al. Variational image compression with a scale hyperprior; 2018. arXiv preprint arXiv:1802.01436.
   Google Scholar
• Minnen D, Balĺe J, Toderici G. Joint autoregressive and hierarchical priors for learned image compression; 2018. arXiv preprint arXiv:1809.02736.
   Google Scholar
• Cheng Z, Sun H, Takeuchi M, et al. Learned image compression with discretized Gaussian mixture likelihoods and attention modules. In: IEEE/CVF, Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13–19. 2020. p. 7939–7948. Computer Vision Foundation/IEEE.
   Google Scholar
• Wintz PA. Transform picture coding. Proc IEEE. 1972;60(7):809–820.
   Web of Science ®Google Scholar
• Rissanen J, Langdon G. Universal modeling and coding. IEEE Trans Inf Theory. 1981;27(1):12–23.
   Web of Science ®Google Scholar
• Liou C-Y, Huang J-C, Yang W-C. Modeling word perception using the elman network. Neurocomputing. 2008;71(16-18):3150–3157.
   Web of Science ®Google Scholar
• Mentzer F, Toderici GD, Tschannen M, et al. High-fidelity generative image compression. Adv Neural Inf Process Syst. 2020;33:11913–11924.
   Google Scholar
• Chollet F. Xception: Deep learning with depthwise separable convolutions; 2017. arXiv:1610.02357.
   Google Scholar
• Nefoussi S, Amamra A, Amarouche IA. A comparative study of deep learning networks for COVID-19 recognition in chest x-ray images. 2020 2nd international workshop on human-centric smart environments for health and well-being (IHSH). Boumerdes, Algeria. IEEE; 2021. p. 237–241.
   Google Scholar
• Yang X, He X, Zhao J, et al. Covid-CT-dataset: a CT scan dataset about covid-19; 2020. arXiv preprint arXiv:2003.13865.
   Google Scholar
• Zuiderveld K. Contrast limited adaptive histogram equalization. Graphics Gems. 1994: 474–485. Academic Press. https://cir.nii.ac.jp/crid/1570009751230513024.
   Google Scholar
• Nefoussi S, Amamra A, Amarouche IA. A comparative study of chest x-ray image enhancement techniques for pneumonia recognition. International Conference on Computing Systems and Applications, Springer. 2020: 276–288.
   Google Scholar
• Cohen JP, Morrison P, Dao L, et al. Covid-19 image data collection: Prospective predictions are the future; 2020. arXiv 2006.11988. https://github.com/ieee8023/covid-chestxray-dataset.
   Google Scholar