• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 34, Pages: 2730-2739

Original Article

Automated System for Prediction and Prognosis of Infection Diseases Using Deep Learning-Based Approaches

Received Date:30 May 2023, Accepted Date:07 August 2023, Published Date:14 September 2023


Objectives: This study explores the potential of deep learning-based techniques to improve disease management and intervention by focusing on their use in infectious disease prediction and prognosis. Methods: The research used deep learning models EfficientNetB0, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2. For this study, a dataset comprising 29,252 images of different diseases such as COVID-19, MERS, Pneumonia, SARS, and tuberculosis. To visualize pixel intensity, exploratory data analysis was performed on the pictures. Preprocessing eliminated disruptive signals via image augmentation and contrast enhancement. After that, Otsu thresholding and contour feature morphological values retrieved relevant features. Findings: The best successful model was found to be EfficientNetB0. During training, it obtained a 90.22% accuracy rate, a loss of 0.279, having an RMSE value of 0.578. However, InceptionResNetV2 showed the best accuracy, loss, and RMSE values throughout model testing. The precise accuracy, loss, and RMSE results were 88%, 0.399, and 0.631, respectively. Novelty: The novelty resides in exploring methods based on deep learning for predicting and prognosticating infectious diseases, with the potential for handling diseases, strategies for intervention, and public health decisions.

Keywords: Tuberculosis; Pneumonia; Infectious diseases; Deep learning; InceptionResNetV2


  1. Santangelo OE, Gentile V, Pizzo S, Giordano D, Cedrone F. Machine learning and prediction of infectious diseases: a systematic review. 2023. Available from: https://doi.org/10.3390/make5010013
  2. Thakur K, Kaur M, Kumar Y. A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases. Archives of Computational Methods in Engineering. 2023;30(7):4477–4497. Available from: https://doi.org/10.1007/s11831-023-09952-7
  3. Çinar A, Yildirim M. Classification of Malaria Cell Images with Deep Learning Architectures. International Information and Engineering Technology Association. 2020;25(1):35–39. Available from: https://doi.org/10.18280/isi.250105
  4. Irmak E. COVID‐19 disease severity assessment using CNN model. IET Image Processing. 2021;15(8):1814–1824. Available from: https://doi.org/10.1049/ipr2.12153
  5. Callejon-Leblic MA, Moreno-Luna R, Cuvillo AD, Reyes-Tejero IM, Garcia-Villaran MA, Santos-Peña M, et al. Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach. Journal of Clinical Medicine. 2021;10(4):570. Available from: https://doi.org/10.3390/jcm10040570
  6. Feng K, He F, Steinmann J, Demirkiran I. Deep-learning Based Approach to Identify Covid-19. SoutheastCon 2021. 2021;p. 1–4. Available from: https://doi.org/10.1109/SoutheastCon45413.2021.9401826
  7. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, et al. Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization. IEEE Access. 2020;8:191586–191601. Available from: https://doi.org/10.1109/ACCESS.2020.3031384
  8. Leo J, Luhanga E, Michael K. Machine Learning Model for Imbalanced Cholera Dataset in Tanzania. The Scientific World Journal. 2019;2019:1–12. Available from: https://doi.org/10.1155/2019/9397578
  9. Tran NK, Albahra S, May L, Waldman S, Crabtree S, Bainbridge S, et al. Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing. Clinical Chemistry. 2021;68(1):125–133. Available from: https://doi.org/10.1093/clinchem/hvab239
  10. Wang M, Wei Z, Jia M, Chen L, Ji H. Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records. BMC Medical Informatics and Decision Making. 2022;22(1):1–3. Available from: https://doi.org/10.1186/s12911-022-01776-y
  11. Taresh MM, Zhu N, Ali TAA, Hameed AS, Mutar ML. Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks. International Journal of Biomedical Imaging. 2021;2021:1–9. Available from: https://doi.org/10.1155/2021/8828404
  12. Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, et al. Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access. 2020;8:132665–132676. Available from: https://doi.org/10.1109/ACCESS.2020.3010287
  13. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018;172(5):1122–1131. Available from: https://doi.org/10.1016/j.cell.2018.02.010
  14. Munadi K, Muchtar K, Maulina N, Pradhan B. Image Enhancement for Tuberculosis Detection Using Deep Learning. IEEE Access. 2020;8:217897–217907. Available from: https://doi.org/10.1109/ACCESS.2020.3041867
  15. Nair K, Deshpande A, Guntuka R, Patil A. Analysing X-Ray Images to Detect Lung Diseases Using DenseNet-169 technique. SSRN Electronic Journal. 2022. Available from: http://dx.doi.org/10.2139/ssrn.4111864
  16. Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in Biology and Medicine. 2021;132:104348. Available from: https://doi.org/10.1016/j.compbiomed.2021.104348
  17. Kaur S, Kumar Y, Koul A, Kamboj SK. A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges. Archives of Computational Methods in Engineering. 2023;30(3):1863–1895. Available from: https://doi.org/10.1007/s11831-022-09853-1
  18. Kaur I, Kumar Y, Sandhu AK, Ijaz MF. Predictive Modeling of Epidemic Diseases Based on Vector-Borne Diseases Using Artificial Intelligence Techniques. Computational Intelligence in Medical Decision Making and Diagnosis. 2023;p. 81–100. Available from: https://doi.org/10.1201/9781003309451-5
  19. Modi K, Singh I, Kumar Y. A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Lifestyle Diseases. Archives of Computational Methods in Engineering. 2023;24:1–24. Available from: https://doi.org/10.1007/s11831-023-09957-2
  20. Daid R, Kumar Y, Gupta A, Kaur I. An effective mechanism for early chronic illness detection using multilayer convolution deep learning predictive modelling. 2021 International Conference on Technological Advancements and Innovations (ICTAI). 2021;p. 649–652. Available from: https://doi.org/10.1109/ICTAI53825.2021.9673393


© 2023 Thakur et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.