Malarial Diagnosis with Deep Learning and Image Processing Approaches

Article Sidebar

Life Cycle of Plasmodium Parasite [25].
PDF
Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6647
Keywords:
Deep learning, CNN, ResNet50, VGG19, noise reduction, image segmentation, GAN, optimization methods

Main Article Content

Mogalraj Kushal Dath
School of Computer Science and Engineering Lovely Professional University Phagwara, India
Nahida Nazir
School of Computer Science and Engineering Lovely Professional University Phagwara, India
Amita Dhankhar
Department of Computer Science and Engineering, University Institute of Engineering anf Technology, Maharshi Dayanand University, Rohtak, Haryana, India
Kamna Solanki
Department of Computer Science and Engineering, University Institute of Engineering anf Technology, Maharshi Dayanand University, Rohtak, Haryana, India
Omdev Dahiya
School of Computer Science and Engineering, Lovely Professional University, Kapurthala, Punjab, India

Abstract

Malaria is a mosquito-borne disease that has killed an estimated a half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, and it also requires the skills of trained laboratory personnel. Additionally, human analysis might make mistakes. Integrating denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique can enhance the performance of diagnosis. Various deep learning models, such as CNN, ResNet50, and VGG19, for recognising the Plasmodium parasite in thick blood smear images have been used. The experimental results indicate that the VGG19 model performed best by achieving 98.46% compared to other approaches. This study demonstrates the potential of artificial intelligence to improve the speed and precision of pathogen detection which is more effective than manual analysis.

Article Details

How to Cite
Dath, M. K. ., Nazir, N. ., Dhankhar, A. ., Solanki, K. ., & Dahiya, O. . (2023). Malarial Diagnosis with Deep Learning and Image Processing Approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 210–222. https://doi.org/10.17762/ijritcc.v11i5s.6647
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

References

Z. Liang and J. X. Huang, “Adaptive Cycle-consistent Adversarial Network for Malaria Blood Cell Image Synthetization,” Proc. - Appl. Imag. Pattern Recognit. Work., vol. 2021-October, 2021, doi: 10.1109/AIPR52630.2021.9762068.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

A. Rahman et al., “Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks,” pp. 1–33, 2019, [Online]. Available: http://arxiv.org/abs/1907.10418

L. Approach, “diagnostics Analyzing Malaria Disease Using E ff ective Deep Learning Approach,” pp. 1–22.

H. A. Nugroho and R. Nurfauzi, “Deep Learning Approach for Malaria Parasite Detection in Thick Blood Smear Images,” 17th Int. Conf. Qual. Res. QIR 2021 Int. Symp. Electr. Comput. Eng., pp. 114–118, 2021, doi: 10.1109/QIR54354.2021.9716198.

B. N. Narayanan, R. A. Ali, and R. C. Hardie, “Performance analysis of machine learning and deep learning architectures for malaria detection on cell images,” no. September, p. 29, 2019, doi: 10.1117/12.2524681.

A. Molina, J. Rodellar, L. Boldú, A. Acevedo, S. Alférez, and A. Merino, “Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks,” Comput. Biol. Med., vol. 136, no. March, 2021, doi: 10.1016/j.compbiomed.2021.104680.

S. Sinha, U. Srivastava, V. Dhiman, P. S. Akhilan, and S. Mishra, “Performance assessment of deep learning procedures: Sequential and ResNet on malaria dataset,” J. Robot. Control, vol. 2, no. 1, pp. 12–18, 2021, doi: 10.18196/jrc.2145.

P. A. Pattanaik, T. Swarnkar, and D. Swain, “Deep filter bridge for malaria identification and classification in microscopic blood smear images,” Int. J. Adv. Intell. Paradig., vol. 20, no. 1–2, pp. 126–137, 2021, doi: 10.1504/IJAIP.2021.117611.

S. Li, Z. Du, X. Meng, and Y. Zhang, “Multi-stage malaria parasite recognition by deep learning,” Gigascience, vol. 10, no. 6, pp. 1–11, 2021, doi: 10.1093/gigascience/giab040.

D. R. Loh, W. X. Yong, J. Yapeter, K. Subburaj, and R. Chandramohanadas, “A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN,” Comput. Med. Imaging Graph., vol. 88, no. December 2020, p. 101845, 2021, doi: 10.1016/j.compmedimag.2020.101845.

K. Pasupa, S. Tungjitnob, and S. Vatathanavaro, “Semi-supervised learning with deep convolutional generative adversarial networks for canine red blood cells morphology classification,” Multimed. Tools Appl., vol. 79, no. 45–46, pp. 34209–34226, 2020, doi: 10.1007/s11042-020-08767-z.

P. A. Pattanaik, M. Mittal, and M. Z. Khan, “Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images,” IEEE Access, vol. 8, pp. 94936–94946, 2020, doi: 10.1109/ACCESS.2020.2996022.

S. Nayak, S. Kumar, and M. Jangid, “Malaria detection using multiple deep learning approaches,” 2019 2nd Int. Conf. Intell. Commun. Comput. Tech. ICCT 2019, pp. 292–297, 2019, doi: 10.1109/ICCT46177.2019.8969046.

I. Journal, “IRJET- Survey of Malaria Detection using Deep Learning”.

Z. Yang, H. Benhabiles, K. Hammoudi, F. Windal, R. He, and D. Collard, “A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images,” Neural Comput. Appl., vol. 34, no. 17, pp. 14223–14238, 2022, doi: 10.1007/s00521-021-06604-4.

K. K. K. Et. al., “An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer,” Turkish J. Comput. Math. Educ., vol. 12, no. 2, pp. 3376–3384, 2021, doi: 10.17762/turcomat.v12i2.2398.

G. B. Cavallari and M. A. Ponti, “Training strategies with unlabeled and few labeled examples under 1-pixel attack by combining supervised and self-supervised learning,” 2022.

I. Kiskin, A. D. Cobb, M. Sinka, K. Willis, and S. J. Roberts, “Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12978 LNAI, pp. 351–366, 2021, doi: 10.1007/978-3-030-86514-6_22.

A. Maqsood, M. S. Farid, M. H. Khan, and M. Grzegorzek, “Deep malaria parasite detection in thin blood smear microscopic images,” Appl. Sci., vol. 11, no. 5, pp. 1–19, 2021, doi: 10.3390/app11052284.

R. Kapoor, “Malaria Detection using Deep Convolutional Neural Network,” 1386.

L. Shi, Z. Guan, C. Liang, and H. You, “Automatic Classification of Plasmodium for Malaria Diagnosis based on Ensemble Neural Network,” ACM Int. Conf. Proceeding Ser., pp. 80–85, 2020, doi: 10.1145/3399637.3399641.

H. Gu, Y. Wang, S. Hong, and G. Gui, “Blind channel identification aided generalized automatic modulation recognition based on deep learning,” IEEE Access, vol. 7, pp. 110722–110729, 2019, doi: 10.1109/ACCESS.2019.2934354.

S. Sakib et al., “Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework,” medRxiv, no. June, p. 2020.11.08.20227819, 2020, [Online]. Available: https://www.medrxiv.org/content/10.1101/2020.11.08.20227819v1%0Ahttps://www.medrxiv.org/content/10.1101/2020.11.08.20227819v1.abstract

I. Marin, S. Mladenovi?, S. Gotovac, and G. Zaharija, “Deep-feature-based approach to marine debris classification,” Appl. Sci., vol. 11, no. 12, pp. 1–25, 2021, doi: 10.3390/app11125644.

Life Cycle of Malaria. (2020). Retrieved from https://www.shutterstock.com/image-vector/life-cycle-malaria-parasite-vector-diagram-1435662671

World Health Organization 2022. https://www.who.int/news-room/fact-sheets/detail/malaria>. Accessed 5 February 2023.

Poostchi, Mahdieh, et al. "Image analysis and machine learning for detecting malaria." Translational Research 194 (2018): 36-55.

Deelder, Wouter, et al. "Using deep learning to identify recent positive selection in malaria parasite sequence data." Malaria journal 20.1 (2021): 270.

Mehanian, Courosh, et al. "Computer-automated malaria diagnosis and quantitation using convolutional neural networks." Proceedings of the IEEE international conference on computer vision workshops. 2017.

Mariki, Martina, Elizabeth Mkoba, and Neema Mduma. "Combining clinical symptoms and patient features for malaria diagnosis: machine learning approach." Applied Artificial Intelligence 36.1 (2022): 2031826.

Hung, Jane, and Anne Carpenter. "Applying faster R-CNN for object detection on malaria images." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.

Joshi, Amogh Manoj, Ananta Kumar Das, and Subhasish Dhal. "Deep learning based approach for malaria detection in blood cell images." 2020 IEEE region 10 conference (TENCON). IEEE, 2020.

QANBAR, Mohanad Mohammed, and Sakir Tasdemir. "Detection of malaria diseases with residual attention network." International Journal of Intelligent Systems and Applications in Engineering 7.4 (2019): 238-244.

Eze, Peter U., and Clement O. Asogwa. "Deep machine learning model trade-offs for malaria elimination in resource-constrained locations." Bioengineering 8.11 (2021): 150.

Kumar, Avinash, Sobhangi Sarkar, and Chittaranjan Pradhan. "Malaria disease detection using cnn technique with sgd, rmsprop and adam optimizers." Deep learning techniques for biomedical and health informatics (2020): 211-230.

Loddo, Andrea, Corrado Fadda, and Cecilia Di Ruberto. "An empirical evaluation of convolutional networks for malaria diagnosis." Journal of Imaging 8.3 (2022): 66.

Zedda, Luca, Andrea Loddo, and Cecilia Di Ruberto. "A deep learning based framework for malaria diagnosis on high variation data set." Image Analysis and Processing–ICIAP 2022: 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part II. Cham: Springer International Publishing, 2022.

Harvey, David, Wessel Valkenburg, and Amara Amara. "Predicting malaria epidemics in Burkina Faso with machine learning." PLoS One 16.6 (2021): e0253302.

Nakasi, Rose, Ernest Mwebaze, and Aminah Zawedde. "Mobile-aware deep learning algorithms for malaria parasites and white blood cells localization in thick blood smears." Algorithms 14.1 (2021): 17.

Dutta, Ashit Kumar, et al. "Barnacles mating optimizer with deep transfer learning enabled biomedical malaria parasite detection and classification." Computational Intelligence and Neuroscience 2022 (2022).