Open Access
·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
• Modelling and Simulation
• Database Management
• Applications to Engineering Sciences
• Applications to Technology
Issues up to 2022 co-published with and available at:
Deep learning and probabilistic neural networks-based identification and classification of Pneumonia-related lung diseases
N. Vinothkumarnvk.cpri@gmail.comDepartment of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore, Tamil Nadu, IndiaView full profile →
, Ch. Madhava Raocmadhavarao@kluniversity.inDepartment of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram, Andhra Pradesh, IndiaView full profile →
, Swati R. Nitnawareswatitidke02@gmail.comDepartment of Electronics Engineering Yeshwantrao Chavan College of EngineeringNagpur, Maharastra, IndiaView full profile →
, M. Shakunthalashakunthalamasi@gmail.comDepartment of Electronics and Communication Engineering R.M.D. Engineering College KavaraipettaiThiruvallur, Tamil Nadu, IndiaView full profile →
, K. V. Shahnazshahnaznitc@gmail.comDepartment of Electronics and Communication Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennai, Tamil Nadu, IndiaView full profile →
, Nelli Sreevidyasreevidya1509@gmail.comDepartment of Information Technology Sreenidhi Institute of Science and TechnologyHyderabad, Telangana, IndiaView full profile →
, *L. BhagyalakshmiCorresponding authorProf.Dr.L.Bhagyalakshmi@gmail.comDepartment of Electronics and Communication Engineering Rajalakshmi Engineering CollegeChennai, Tamil Nadu, IndiaView full profile →
, Nitesh Kumarrickyengineer@gmail.comDepartment of Information Technology L. N. Mishra College of Business ManagementMuzaffarpur, Bihar, IndiaView full profile →
* Corresponding author · click or hover a name for details
In every part of the world, lung disease is a major issue. Early lung disease identification is crucial. A variety of deep learning techniques, such as convolutional neural networks (CNNs), Visual Geometry Group (VGG) networks, capsule networks, and traditional neural networks, are commonly employed for predicting lung diseases. Since the emergence of the novel COVID-19, extensive research has been conducted to explore its ability to make precise predictions. Given that many fatalities were attributed to severe chest congestion, Pneumonia an early-stage lung condition is likely closely associated with COVID-19 (pneumonic condition). Medical professionals may find it difficult to differentiate COVID-19 from other lung diseases, such as Pneumonia.
[1] S. Bharati, P. Podder, R. Mondal, A. Mahmood, and M. Raihan-Al-Masud, “Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer,” Advances in Intelligent Systems and Computing, vol. 941, Springer, pp. 447–457 (2020), doi: 10.1007/978-3-030-16660-1_44.[2] N. Coudray, P. S. Ocampo, T. Sakellaropoulos, M. A. L. O’Donovan, E. A. Gilmore, J. W. Jones, L. B. Roberts, G. D. Doyle, and D. M. E. De Souza, “Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning,” Nat. Med., vol. 24, pp. 1559–1567 (2018), doi: 10.1038/s41591-018-0177-5.[3] M. R. H. Mondal, S. Bharati, P. Podder, and P. Podder, “Data analytics for novel coronavirus disease,” Informatics in Medicine Unlocked, vol. 20, Elsevier, p. 100374 (2020), doi: 10.1016/j.imu.2020.100374.[4] K. Kuan, M. Ravaut, G. Manek, H. Chen, J. Lin, B. Nazir, C. Chen, T. C. Howe, Z. Zeng, and V. Chandrasekhar, “Deep learning for lung cancer detection: tackling the Kaggle data science bowl 2017 challenge,” (2017). [Online]. Available: https://arxiv.org/abs/1705.09435.[5] W. Sun, B. Zheng, and W. Qian, “Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis,” Comput. Biol. Med., vol. 89, pp. 530–539 (2017).[6] Q. Song, L. Zhao, X. Luo, and X. Dou, “Using deep learning for classification of lung nodules on computed tomography images,” J. Healthcare Eng., vol. 2017, Art. ID 8314740 (2017), doi: 10.1155/2017/8314740.[7] W. Sun, B. Zheng, and W. Qian, “Computer aided lung cancer diagnosis with deep learning algorithms,” in Proc. SPIE Medical Imaging, vol. 9785, pp. 97850Z (2016), doi: 10.1117/12.2216307.[8] NIH sample Chest X-rays dataset. [Online]. Available: https://www.kaggle.com/nih-chest-xrays/sample. [Accessed: June 28, 2020].[9] A. Abbas, M. M. Abdelsamea, and M. M. Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,” Appl. Intell. (2020), doi: 10.1007/s10489-020-01829-7.[10] N. Kumar, K. Premika, L. Bhagyalakshmi, and S. K. Suman, “Smart Traffic Rescuer using IOT,” J. Pharm. Sci. Res., vol. Special Issue 8, pp. 215–219 (2017).[11] S. K. Suman, N. Arivazhagan, L. Bhagyalakshmi, K. A. S. L. Gajendra, A. D. Pradeep, P. S. K. S. Ranjith, and T. K. K. Rajesh, “Detection and Prediction of HMS from Drinking Water by Analyzing the Adsorbents from Residuals Using Deep Learning,” Adsorption Sci. Technol., vol. 2022, Art. ID 3265366 (2022), doi: 10.1155/2022/3265366.
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