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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

Abnormality detection in chest diseases using a convolutional neural network

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* Corresponding author · click or hover a name for details

pp. 97–111Vol. 44Issue 1December 2022DOI: 10.47974/JIOS-1298XML
Published Online:
31 Dec 2022
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1298
Pages:
97–111

Abstract

It has developed into a new branch of medicine where doctors and researchers are continually working on numerous issues linked to lung ailments. This field of medicine studies lung abnormalities through chest pictures and classifies them using artificial intelligence and machine learning techniques. As graphics drives and data storage systems have gotten more prevalent and sophisticated, enormous amounts of medical picture data are now being produced. Despite the fact that there have been several studies in the medical profession over the past ten years, this particular area has not received enough attention because of a lack of data. Numerous researchers have employed custom feature learning algorithms, but the results have not been sufficiently useful for real-world applications. The employed technique is advantageous in bridging the semantic gap between low-stage records obtained by imaging tools and high-stage information preserved by living things. With five layers—a convolutional layer, an activation layer, a pooling layer, an absolutely linked layer, and a softmax layer—that decide the output probability for each component, we created a far better convolutional neural network that enables accurate X-ray image type. We built our model utilizing a set of facts that are freely accessible. To be honest, our version performs a respectable job at diagnosing conditions related to the chest. Comparative observations show that the proposed model is more suited for the class of chest-related problems.

Keywords

Deep learningClassificationConvolutional neural network (CNN)Class-selective relevance mapping (CRM)Chest X-Ray

Subject Classifications

68T0568T0968T07

References

[1] Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S. & Navab, N. (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images, IEEE Transactions on Medical Imaging, 35(5), 1313–1321.
[2] Anthimopoulos, Christodoulidis, S., Christe, A. & Mougiakakou, S. (2014). Classification of interstitial lung disease patterns using local DCT features and random forest,” 36th Annual International Conference of the IEEE Engineering Medicine and Biology Society, PMID: 25571374, 6040–6043.
[3] Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Transactions on Medical Imaging, 35(5), 1207–1216.
[4] Anthopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. & Mougiakakou, S. (216). Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network, IEEE Transactions on Medical Imaging, 35(5), 1207-1216.
[5] Chaudhary, A., Hazra, A. & Chaudhary, P. (2019). Diagnosis of chest diseases in x-ray images using deep convolutional neural network, 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 1-6.
[6] Choudhary, P. & Hazra, A., (2019). Chest disease radiography in twofold: using convolutional neural networks and transfer learning. Evolving Systems, 1-13.
[7] Cun, B. B. L., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. & Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network, NIPS.
[8] Deng, L. & Yu, D. (2014). Deep Learning: Methods and Applications, Foundations and Trends in Signal Processing: 7(3–4),197-387. http://dx.doi.org/10.1561/2000000039.
[9] Depeursinge, A., Ville, D. V. D., Platon, A., Geissbuhler, A., Poletti, P.A. & Muller, H. (2012). Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames, IEEE Transactions on Information Technology in Biomedicine, 16(4), 665–675.
[10] Foncubierta-Rodríguez A., Depeursinge, A. & Müller, H. (2012). Using Multiscale Visual Words for Lung Texture Classification and Retrieval, Medical Content-Based Retrieval, Lecture Notes in Computer Science, 7075, 69-79.
[11] Fu, H., Cheng, H., Xu, Y., Wong, D. W. K., Liu, J. & Cao, X. (2018). Joint optic disc and cup segmentation based on multi-label deep network and polar transformation, IEEE Transactions on Medical Imaging, 1-9, https://arxiv.org/pdf/1801.00926.pdf
[12] Fu, H., Xu, Y., Lin, S., Zhang, X., Wong, D. W. K., Liu, J., Frangi, A. F., Baskaran, M. & Aung, T. (2017). Segmentation and quantification for angleclosure glaucoma assessment in anterior segment oct, IEEE Transactions on Medical Imaging, 36(9), 1930–1938.
[13] Gangeh, Mehrdad J., Sørensen, L., Shaker, S. B., Kamel, M. S., Bruijne, M. D. & Loog, M. (2010). A texton-based approach for the classification of lung parenchyma in CT images, Medical Image Computing and Computer- Assisted Intervention (MICCAI), Springer, 595-602.
[14] Glorot, X. & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks, AISTATS, 249-256.
[15] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J. & Chen, T. (2017). Recent advances in convolutional neural networks, Computer Vision and Pattern Recognition, https://doi.org/10.48550/arXiv.1512.07108.
[16] Hazra, A. (2021). A comprehensive survey on chest diseases analysis: technique, challenges and future research directions, International Journal of Multimedia Information Retrieval, 10, 83–110.
[17] Hazra, A., Choudhary, P. and Singh, M.S., (2021). Recent advances in deep learning techniques and its applications: an overview. Advances in Biomedical Engineering and Technology, 103-122.
[18] Hazra, A., Choudhary, P., Inunganbi, S. and Adhikari, M., (2021). Bangla-Meitei Mayek scripts handwritten character recognition using convolutional neural network. Applied Intelligence, 51(4), 2291-2311.
[19] https://academictorrents.com/details/e615d3aebce373f1dc8bd9d1 1064da55bdadede0. Bounding boxes for ~1000 images (BBox_List_2017.csv): Image Index, Finding Label, Bbox[x, y, w, h]. [x y] are coordinates of each box’s top left corner. [w h] represent the width and height of each box.
[20] https://www.kaggle.com/crawford.
[21] https://www.kaggle.com/paultimothymooney.
[22] https://www.youtube.com/channel/UCjAOM-s-f2YfeLNGGZuY-Bg/featured.
[23] Hubel, D. H. & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex, The Journal of Physiology, 195, 215-243.
[24] Korfiatis, P. D., Karahaliou, A. N., Kazantzi, A. D., Kalogeropoulou, C. & Costaridou, L. I. (2010). Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT, IEEE Transactions on Information Technology in Biomedicine,14(3), 675–680.
[25] Krizhevsky, A., Sutskever, I. & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks, NeurIPS Proceedings: Part of Advances in Neural Information Processing Systems, 9.
[26] Kumar, P., Grewal, M. & Srivastava, M. M. (2017). Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs, arXiv preprint arXiv:1711.08760.
[27] Lauge, S., Shaker, S. B. & Bruijne, M. D. (2010). Quantitative analysis of pulmonary emphysema using local binary patterns, IEEE Transactions on Medical Imaging, 29(2), 559-569.
[28] Li, Q., Cai, W. & Feng, D. D. (2013). Lung image patch classification with automatic feature learning, Proceedings: IEEE Annual International Conference Engineering Medicine Biology Society, 6079–6082.
[29] Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D. & Chen, M. (2014). Medical image classification with convolutional neural network,” in 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 844–848.
[30] Liao, R., Miao, S., Tournemire, P. D., Grbic, S., Kamen, A., Mansi, T. & Comaniciu, D. (2017). An artificial agent for robust image registration, In AAAI, 4168–4175.
[31] Liskowski, P. & Krawiec, K. (2016). Segmenting retinal blood vessels with¡? pub newline?¿ deep neural networks, IEEE Transactions on Medical Imaging, 35(11), 2369–2380.
[32] Moeskops, P., Viergeve, M.A., Mendrik, A. M., Vries, L., S., D., Benders, M. J. N. L. & Isgum, I. (2016). Automatic segmentation of MR brain images with a convolutional neural network, IEEE Transactions on Medical Imaging, 35(5), 1252-1261.
[33] Open-i: An open access biomedical search engine. https//openi.nlm.nih.gov.2,3,4.
[34] Rajpurkar, P. , Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. & Shpanskaya, K. (2017). Chexnet: Radiologist level pneumonia detection on chest x-rays with deep learning,” arXiv preprint arXiv:1711.05225.
[35] Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B. & Summers, R. M. (2015). Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 556–564.
[36] Simonyan, K. & Zisserman, A. (1999). Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations. https://arxiv.org/pdf/1409.1556.pdf.
[37] Goel, D., Gulati, Payal, Jha, S. K., Kumar, N. K. & Khan, A. (2022). Evaluation of Machine Learning Techniques for Crop Yield Prediction, Artificial Intelligence Applications in Agriculture and Food Quality Improvement, 112-128, DOI: 10.4018/978-1-6684-5141-0.ch007.
[38] Tripathi, P., Kumar, N., Rai, M. & Khan, A. (2022). Applications of Deep Learning in Agriculture, Artificial Intelligence Applications in Agriculture and Food Quality Improvement, 17-28, DOI: 10.4018/978-1-6684-5141-0.ch002.
[39] Kumar, N., Tripathi, P. & Alam, K. (2016). Non-Negative Matrix Factorization-Based EEG Signal Classification, Indian Journal of Industrial and Applied Mathematics, 7(2), 212–219.
[40] Simonyan, K. & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations. https://arxiv.org/pdf/1409.1556.pdf
[41] Sluimer, C., Waes, P. F. V., Viergever, M. A. & Ginneken, B. V. (2003). Computer-aided diagnosis in high resolution CT of the lungs, Medical Physics, 30(12), 3081–3090.
[42] Song, Y., Cai, W., Zhou, Y. & Feng, D. D. (2013). Feature-based image patch approximation for lung tissue classification,” IEEE Trans. Med. Imaging, vol. 32, no. 4, pp. 797–808, 2013.
[43] Stefan, D., Reichenbecher, M. A. K., Zuna, I., Schlegel, W. & Kaick, G. V. (1997). Usual interstitial pneumonia: quantitative assessment of high-resolution computed tomography findings by computer-assisted texture-based image analysis, Investigative Radiology, 32(9), 566-574.
[44] Tulder, G. V. & Bruijne, M. D. (2016). Combining Generative and Discriminative Representation Learning for Lung CT Analysis with Convolutional Restricted Boltzmann Machines, IEEE Transactions on Medical Imaging, 35(5), 1262-1272.
[45] Uchiyama, Y., Katsuragawa, S., Abe, H., Shiraishi, J., Li, F., Li, Q., Zhang, C.-T. , Suzuki, K. & Doi, K. (2003). Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography, Medical Physics, 30(9), 2440–2454.
[46] Uppaluri, R., Hoffman, E., A., Sonka, M., Hartley, P. G., Hunninghake, G. W. & McLennan, G. (1999). Computer recognition of regional lung disease patterns, American Journal of Respiratory and Critical Care Medicine, 160(2), 648–654.
[47] Vo, K. T. & Sowmya, A. (2010). Multiple kernel learning for classification of diffuse lung disease using HRCT lung images, IEEE Conference Proceedings Engineering Medicine Biology Society, 3085–3088.
[48] Wan, J., Wang, D., Hoi, S. C. H. & Wu, P. (2014). Deep learning for content-based image retrieval: A comprehensive study, Proceedings of the 22nd ACM international conference on Multimedia, 157-166.
[49] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M. & Summers, R. M. (2017). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly- Supervised Classification and Localization of Common Thorax Diseases, IEEE CVPR, 3462-3471.
[50] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M. & Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” arXiv preprint arXiv:1705.02315.
[51] Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., Eric, I. & Chang, C. (2014). Deep learning of feature representation with multiple instance learning for medical image analysis, Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference, 1626–1630.
[52] Xu, Y., van Beek, E. J. R., Hwanjo, Y., Guo, J., McLennan, G. & Hoffman, E. A. (2006). Computer-aided Classification of Interstitial Lung Diseases Via MDCT: 3D Adaptive Multiple Feature Method (3D AMFM Academic Radiology, 13(8), 969–978.
[53] Y. Song, W. Cai, Y. Zhou, and D. D. Feng, “Feature-based image patch approximation for lung tissue classification, IEEE Transactions on Medical Imaging, 32(4), 797–808.
[54] Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D. N. & Zhou, X. S. (2016). Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition, IEEE transactions on medical imaging, 35(5), 1332-1343.
[55] Yang, D., Zhang, S., Yan, Z., Tan, C., Li, K. & Metaxas, D. (2015). Automated anatomical landmark detection ondistal femur surface using convolutional neural network, IEEE 12th International Symposium Biomedical Imaging (ISBI), 17–21.
[56] Younes, Y. (1999). On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates, Stochastics: International Journal of Probability and Stochastic Processes, 65(3-4), 177–228.
[57] Yuan, Y., Chao, M. & Lo, Y.-C. (2017). Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance, IEEE Transactions on Medical Imaging, 36(9):1876-1886.
[58] Zhao, W., Xu, R., Hirano, Y., Tachibana, R. & Kido, S. (2013). Classification of diffuse lung diseases patterns by a sparse representation based method on HRCT images, IEEE Annual International Conference Engineering Medicine Biology, 5457-5460.
[59] Kalra, S. & Leekha, A. (2020). Survey of convolutional neural networks for image captioning, Journal of Information and Optimization Sciences, 41(1), 239-260.
[60] Sarin, N., Chug, A. & Singh, A. P. (2022). An image based prediction system for early blight disease in tomato plants using deep learning algorithm, Journal of Information and Optimization Sciences, 43(4), 761-779.
[61] Sahoo, D. K., Das, A., Mohanty, M. N. & Mishra, S. (2022). Brain tumor detection using inpainting and deep ensemble model, Journal of Information and Optimization Sciences, https://doi.org/10.1080/02522667.2022.2091094.

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