[1]Xu, Y., Souza, L. F. F., Silva, I. C. L., Marques, A. G., Silva, F. H. S., Nunes, V. X., Han, T.,Jia, C., de Albuquerque, V. H. C., & Filho, P. P. R. (2021). A soft computing automatic based in deep learning with use of fine-tuning for pulmonary segmentation in computed tomography images. Applied Soft Computing, 112, 107810. https://doi.org/10.1016/J.ASOC.2021.107810
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[3] Verma, D., Bose, C., Tufchi, N., Pant, K., Tripathi, V., & Thapliyal, A. (2020). An efficient framework for identification of Tuberculosis and Pneumonia in chest X-ray images using Neural Network. Procedia Computer Science, 171, 217–224. https://doi.org/10.1016/J.PROCS.2020.04.023
[4] Prakash, N. B., Murugappan, M., Hemalakshmi, G. R., Jayalakshmi, M., & Mahmud, M. (2021). Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation. Sustainable Cities and Society, 75. https://doi.org/10.1016/j.scs.2021.103252
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[6] Park, B., Park, H., Lee, S. M., Seo, J. B., & Kim, N. (2019). Lung Segmentation on HRCT and Volumetric CT for Diffuse Interstitial Lung Disease Using Deep Convolutional Neural Networks. Journal of digital imaging, 32(6), 1019–1026. https://doi.org/10.1007/s10278-019-00254-8
[7] Oulefki, A., Agaian, S., Trongtirakul, T., & Kassah Laouar, A. (2021). Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognition, 114. https://doi.org/10.1016/j.patcog.2020.107747
[8] M. Liu, J. Dong, X. Dong,H. Yu and L. Qi, "Segmentation of Lung Nodule in CT Images Based on Mask R-CNN," 2018 9th International Conference on Awareness Science and Technology (iCAST), 2018, pp. 1-6, doi: 10.1109/ICAwST.2018.8517248.
[9]Siti Nurmaini, Alexander Edo Tondas, Radiyati Umi Partan, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Firdaus Firdaus, Bambang Tutuko, Rachmat Hidayat, and Ade Iriani Sapitri, "Automated Detection of COVID-19 Infected Lesion on Computed Tomography Images Using Faster-RCNNs," Engineering Letters, vol. 28, no.4, pp1295-1301, 2020
[10]Kopelowitz, E., & Engelhard, G. (2019). Lung Nodules Detection and Segmentation Using 3D Mask-RCNN. http://arxiv.org/abs/1907.07676
[11]Gao, X. W., James-Reynolds, C., & Currie, E. (2020). Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing, 392, 233–244. https://doi.org/10.1016/j.neucom.2018.12.086
[12] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. http://arxiv.org/abs/1703.06870
[13] Rosenthal A, Gabrielian A, Engle E, Hurt DE, Alexandru S, Crudu V, et al. The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis. J Clin Microbiol. 2017;55(11):3267-82
[14] Armato, S. G., Mclennan, G., Bidaut, L., Mcnitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A.,Macmahon, H., van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., & Brown, M. S. (2011). The Lung Image Database Consortium "LIDC... and Image Database Resource Initiative "IDRI...: A Completed Reference Database of Lung Nodules on CT Scans.
[15] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. http://arxiv.org/abs/1506.01497.
[16] Jaiswal, A. K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A., & Rodrigues, J. J.P. C. (2019). Identifying pneumonia in chest X-rays: A deep learning approach. Measurement: Journal of the International Measurement Confederation, 145, 511–518. https://doi.org/10.1016/j.measurement.2019.05
[17] Nurmaini, S., Rachmatullah, M. N., Sapitri, A. I., Darmawahyuni, A., Jovandy, A., Firdaus, F., Tutuko, B., & Passarella, R. (2020). Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation. IEEE Access, 8, 196160–196174. https://doi.org/10.1109/ACCESS.2020.3034367
[18] Zuo, L., He, P., Zhang, C., & Zhang, Z. (2020). A robust approach to reading recognition of pointer meters based on improved mask-RCNN. Neurocomputing, 388, 90–101. https://doi.org/10.1016/j.neucom.2020.01.032
[19] Gamage, H. V. L. C., Wijesinghe, W. O. K. I. S., & Perera, I. (2019). Instance-Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask-RCNN. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11731 LNCS, 511–522. https://doi.org/10.1007/978-3-030-30493-5_49
[20] Zhang, Z., Yin, X., & Yan, Z. (2022). Rapid data annotation for sand-like granular instance segmentation using mask-RCNN. Automationin Construction, 133. https://doi.org/10.1016/j.autcon.2021.103994
[21] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2016). Feature Pyramid Networks for Object Detection. http://arxiv.org/abs/1612.03144
[22] Evain, E., Raynaud, C., Ciofolo-Veit, C., Popoff, A., Caramella, T., Kbaier, P., Balleyguier, C., Harguem-Zayani, S., Dapvril, H., Ceugnart, L., Monroc, M., Chamming’s, F., Doutriaux-Dumoulin, I., Thomassin-Naggara, I., Haquin, A., Charlot, M., Orabona, J., Fourquet,T., Bousaid, I., ... Olivier, A. (2021). Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation. Diagnostic and Interventional Imaging, 102(11), 653–658. https://doi.org/10.1016/j.diii.2021.09.002
[23] Khan, M. A., Akram, T., Zhang, Y. D., & Sharif, M. (2021). Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognition Letters, 143, 58–66. https://doi.org/10.1016/j.patrec.2020.12.015
[24] Shenavarmasouleh, F., & Arabnia, H. R. (2020). DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning. http://arxiv.org/abs/2007.02026
[25] He, K., Zhang, X., Ren, S.,& Sun, J. (2015). Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385
[2] Wang, D., Zhang, T., Li, M., Bueno, R., & Jayender, J. (2021). 3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation. Computerized Medical Imaging and Graphics, 88. https://doi.org/10.1016/j.compmedimag.2020.101814
[3] Verma, D., Bose, C., Tufchi, N., Pant, K., Tripathi, V., & Thapliyal, A. (2020). An efficient framework for identification of Tuberculosis and Pneumonia in chest X-ray images using Neural Network. Procedia Computer Science, 171, 217–224. https://doi.org/10.1016/J.PROCS.2020.04.023
[4] Prakash, N. B., Murugappan, M., Hemalakshmi, G. R., Jayalakshmi, M., & Mahmud, M. (2021). Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation. Sustainable Cities and Society, 75. https://doi.org/10.1016/j.scs.2021.103252
[5] Mamalakis, M., Swift, A. J., Vorselaars, B., Ray, S., Weeks, S., Ding, W., Clayton, R. H., Mackenzie, L. S., & Banerjee, A. (2021). DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays. Computerized Medical Imaging and Graphics, 102008. https://doi.org/10.1016/j.compmedimag.2021.102008
[6] Park, B., Park, H., Lee, S. M., Seo, J. B., & Kim, N. (2019). Lung Segmentation on HRCT and Volumetric CT for Diffuse Interstitial Lung Disease Using Deep Convolutional Neural Networks. Journal of digital imaging, 32(6), 1019–1026. https://doi.org/10.1007/s10278-019-00254-8
[7] Oulefki, A., Agaian, S., Trongtirakul, T., & Kassah Laouar, A. (2021). Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognition, 114. https://doi.org/10.1016/j.patcog.2020.107747
[8] M. Liu, J. Dong, X. Dong,H. Yu and L. Qi, "Segmentation of Lung Nodule in CT Images Based on Mask R-CNN," 2018 9th International Conference on Awareness Science and Technology (iCAST), 2018, pp. 1-6, doi: 10.1109/ICAwST.2018.8517248.
[9]Siti Nurmaini, Alexander Edo Tondas, Radiyati Umi Partan, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Firdaus Firdaus, Bambang Tutuko, Rachmat Hidayat, and Ade Iriani Sapitri, "Automated Detection of COVID-19 Infected Lesion on Computed Tomography Images Using Faster-RCNNs," Engineering Letters, vol. 28, no.4, pp1295-1301, 2020
[10]Kopelowitz, E., & Engelhard, G. (2019). Lung Nodules Detection and Segmentation Using 3D Mask-RCNN. http://arxiv.org/abs/1907.07676
[11]Gao, X. W., James-Reynolds, C., & Currie, E. (2020). Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing, 392, 233–244. https://doi.org/10.1016/j.neucom.2018.12.086
[12] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. http://arxiv.org/abs/1703.06870
[13] Rosenthal A, Gabrielian A, Engle E, Hurt DE, Alexandru S, Crudu V, et al. The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis. J Clin Microbiol. 2017;55(11):3267-82
[14] Armato, S. G., Mclennan, G., Bidaut, L., Mcnitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A.,Macmahon, H., van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., & Brown, M. S. (2011). The Lung Image Database Consortium "LIDC... and Image Database Resource Initiative "IDRI...: A Completed Reference Database of Lung Nodules on CT Scans.
[15] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. http://arxiv.org/abs/1506.01497.
[16] Jaiswal, A. K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A., & Rodrigues, J. J.P. C. (2019). Identifying pneumonia in chest X-rays: A deep learning approach. Measurement: Journal of the International Measurement Confederation, 145, 511–518. https://doi.org/10.1016/j.measurement.2019.05
[17] Nurmaini, S., Rachmatullah, M. N., Sapitri, A. I., Darmawahyuni, A., Jovandy, A., Firdaus, F., Tutuko, B., & Passarella, R. (2020). Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation. IEEE Access, 8, 196160–196174. https://doi.org/10.1109/ACCESS.2020.3034367
[18] Zuo, L., He, P., Zhang, C., & Zhang, Z. (2020). A robust approach to reading recognition of pointer meters based on improved mask-RCNN. Neurocomputing, 388, 90–101. https://doi.org/10.1016/j.neucom.2020.01.032
[19] Gamage, H. V. L. C., Wijesinghe, W. O. K. I. S., & Perera, I. (2019). Instance-Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask-RCNN. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11731 LNCS, 511–522. https://doi.org/10.1007/978-3-030-30493-5_49
[20] Zhang, Z., Yin, X., & Yan, Z. (2022). Rapid data annotation for sand-like granular instance segmentation using mask-RCNN. Automationin Construction, 133. https://doi.org/10.1016/j.autcon.2021.103994
[21] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2016). Feature Pyramid Networks for Object Detection. http://arxiv.org/abs/1612.03144
[22] Evain, E., Raynaud, C., Ciofolo-Veit, C., Popoff, A., Caramella, T., Kbaier, P., Balleyguier, C., Harguem-Zayani, S., Dapvril, H., Ceugnart, L., Monroc, M., Chamming’s, F., Doutriaux-Dumoulin, I., Thomassin-Naggara, I., Haquin, A., Charlot, M., Orabona, J., Fourquet,T., Bousaid, I., ... Olivier, A. (2021). Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation. Diagnostic and Interventional Imaging, 102(11), 653–658. https://doi.org/10.1016/j.diii.2021.09.002
[23] Khan, M. A., Akram, T., Zhang, Y. D., & Sharif, M. (2021). Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognition Letters, 143, 58–66. https://doi.org/10.1016/j.patrec.2020.12.015
[24] Shenavarmasouleh, F., & Arabnia, H. R. (2020). DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning. http://arxiv.org/abs/2007.02026
[25] He, K., Zhang, X., Ren, S.,& Sun, J. (2015). Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385
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Affiliations
Muhammad Arnaldo
Universitas Sriwijaya
Siti Nurmaini
Intelligent System Research Grup, Universitas Sriwijaya
Hadipurnawan Satria
Department of Informatics, Faculty of Computer Science, Universitas Sriwijaya
Muhammad Naufal Rachmatullah
Intelligent System Research Grup, Universitas Sriwijaya
Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network
Abstract
Pulmonary disease has affected tens of millions of people in the world. This disease has also become the cause of death of millions of its sufferers every year. In addition, lung disease has also become the cause of other respiratory complications, which also causes the death of the sufferer. The diagnosis of pulmonary diseases through medical imaging is a significant challenge in computer vision and medical image processing. The difficulty is due to the wide variety in infected areas' shape, dimension, and location. Another challenge is to differentiate one lung disease from the other. Discriminating pulmonary diseases is a notable concern in the diagnosis of pulmonary disease. We have adopted the deep learning convolutional neural network in this study to address these challenges. Seven models were constructed using the Mask Region-based Convolutional Neural Network (Mask-RCNN) architecture to detect and segment infected areas within the lung region from CT scan imagery. The evaluation results show that the best model obtained scores of 91.98%, 85.25%, and 93.75% for DSC, MIoU, and mAP, respectively. The segmentation results are then visualized.