The International Arab Journal of Information Technology (IAJIT)


Malaria Parasite Detection on Microscopic Blood Smear Images with Integrated Deep Learning Algorithms

Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is difficult to detect due to inadequate equipment and technology. Microscopic check-ups of blood smear images by experts help to detect malaria-infected parasites accurately. However, manual analysis is tedious and time-consuming as the experts have to deal with many cases. This paper presents computer assisted malaria parasite detection model by classifying the blood smear image with hybrid deep learning methods that have high accuracy for classification. In the proposed approach the blood smear images are pre-processed using bilateral filtering technique in which features are extracted with the convolutional neural network. These features are selected by the improved grey-wolf optimization, and image classification is performed with the support vector machine. To evaluate the efficiency of the proposed technique, the NIH malaria dataset is utilized and the results are compared with existing approaches in terms of accuracy, F-Measure, recall, precision, and specificity. The outcome reveals that the proposed scheme is accurate and can be more helpful to pathologists for reliable parasite detection.

[1] Bibin D., Nair M., and Punitha P., “Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks,” IEEE Access, vol. 5, pp. 9099-9108, 2017.

[2] Das D., Ghosh M., Pal M., Maiti A., and Chakraborty C., “Machine Learning Approach for Automated Screening of Malaria Parasite Using Light Microscopic Images,” Micron, vol. 45, pp. 97-106, 2013.

[3] Fatima T. and Farid M., “Automatic Detection of Plasmodium Parasites from Microscopic Blood Images,” Journal of Parasitic Diseases, vol. 44, no. 1, pp. 69-78, 2020.

[4] Go T., Kim J., Byeon H., and Lee S., “Machine Learning-Based in-Line Holographic Sensing of Unstained Malaria-Infected Red Blood Cells,” Journal of Biophotonics, vol. 11, no. 9, pp. e201800101, 2018. 178 The International Arab Journal of Information Technology, Vol. 20, No. 2, March 2023

[5] Gomez-Aguilar J., Cordova-Fraga T., Abdeljawad T., Khan A., and Khan H., “Analysis of Fractal-Fractional Malaria Transmission Model,” Fractals, vol. 28, no. 4, pp. 2040041, 2020.

[6] Jan Z., Khan A., Sajjad M., Muhammad K., Rho S and Mehmood I., “A Review on Automated Diagnosis of Malaria Parasite in Microscopic Blood Smears Images,” Multimedia Tools and Applications, vol. 77, no. 8, pp. 9801-9826, 2018.

[7] Jong-Dae K., Kyeong-Min N., Chan-Young P., Yu-Seop K., and Hye-Jeong S., “Automatic Detection of Malaria Parasite in Blood Images using Two Parameters,” Technology and Health Care, vol. 24, no. 1, pp. 33-39, 2016.

[8] Kim K., Yoon H., Diez-Silva M., Dao M., Dasari R., and Park Y., “High-Resolution Three- Dimensional Imaging of Red Blood Cells Parasitized by Plasmodium Falciparum and in Situ Hemozoin Crystals Using Optical Diffraction Tomography,” Journal of Biomedical Optics, vol. 19, no. 1, pp. 011005, 2013.

[9] Krizhevsky A., Sutskeve I., and Hinton G., “ImageNet Classification with Deep Convolutional Neural Networks Mark,” Communication of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

[10] Kumar H. and Tolia N., “Getting in: the Structural Biology of Malaria Invasion,” PLoS pathogens, vo1. 15, no. 9, 2019.

[11] Maity M., Jaiswal A., Gantait K., Chatterjee J., and Mukherjee A., “Quantification of Malaria Parasitaemia using Trainable Semantic Segmentation and Capsnet,” Pattern Recognition Letters, vol. 138, pp. 88-94, 2020.

[12] Manescu P., Shaw M., Elmi M., Neary-Zajiczek L., Claveau R., Pawar V., and Oladejo B., “Expert-Level Automated Malaria Diagnosis on Routine Blood Films with Deep Neural Networks,” American Journal of Hematology, vol. 95, no. 8, pp. 883-891, 2020.

[13] Memon M., Khanzada T.., Memon S., and Hassan S., “Blood Image Analysis to Detect Malaria Using Filtering Image Edges and Classification,” Telecommunication Computing Electronics and Control , vol. 17, no. 1, pp. 194- 201, 2019.

[14] Mirjalili S., Mirjalili S., and Lewis A., “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014.

[15] Park H., Rinehart M., Walzer K., Chi J., and Wax A., “Automated Detection of P. Falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells,” PloS one, vol. 11, no. 9, 2016.

[16] Pattanaik P., Mittal M., Khan M., and Panda S., “Malaria Detection Using Deep Residual Networks with Mobile Microscopy,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 5, pp. 1700-1705, 2020.

[17] Pillay E., Khodaiji S., Bezuidenhout B., Litshie M., and Coetzer T., “Evaluation of Automated Malaria Diagnosis using the Sysmex XN-30 Analyser in A Clinical Setting,” Malaria Journal, vol. 18, no.1, pp. 1-14, 2019.

[18] Rehman A., Abbas N., Saba T., Mehmood Z., Mahmood T., and Ahmed K., “Microscopic Malaria Parasitemia Diagnosis and Grading on Benchmark Datasets,” Microscopy Research and Technique, vol. 81, no. 9, pp. 1042-1058, 2018.

[19] Rosado L., Da-Costa J., Elias D., and Cardoso J., “A Review of Automatic Malaria Parasites Detection and Segmentation in Microscopic Images,” Anti-Infective Agents, vol. 14, no. 1, pp. 11-22, 2016.

[20] Sajana T. and Narasingarao M., “Classification of Imbalanced Malaria Disease Using Naïve Bayesian Algorithm,” International Journal of Engineering and Technology, vol. 7, no. 2.7, pp. 786-790, 2018.

[21] Shah D., Kawale K., Shah M., Randive S., and Mapari R., “Malaria Parasite Detection Using Deep Learning: (Beneficial to humankind),” in Proceedings of the 4th International Conference on Intelligent Computing and Control Systems, Madurai, pp. 984-988, 2020.

[22] Sharma P., Banerjee S., Tiwari D., and Patni J., C., “Machine Learning Model for Credit Card Fraud Detection-A Comparative Analysis,” The International Arab Journal of Information Technology, vol.18, no. 6, pp. 789-796, 2021.

[23] Simonyan K. and Zisserman A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Proceedings of the International Conference on Learning Representations, San Diego, pp. 1-14, 2015.

[24] Singlaa N. and Srivastava V., “Deep Learning Enabled Multi-Wavelength Spatial Coherence Microscope for the Classification of Malaria- Infected Stages with Limited Labelled Data Size,” Optics and Laser Technology, vol. 130, pp. 106335, 2020.

[25] Sonibare O., Bello I., Olowookere S., Shabi O., and Makinde N., “Effect of Malaria Preventive Education on the Use of Long-Lasting Insecticidal Nets Among Pregnant Females in A Teaching Hospital in Osun State, South-West Nigeria,” Parasite Epidemiology and Control, vol. 11, 2020.

[26] Torres K., Bachman C., Delahunt C., Baldeon J., Alava F., Vilela D., Ostbye T., and et al., “Automated Microscopy for Routine Malaria Diagnosis: A Field Comparison on Giemsa- Stained Blood Films in Peru,” Malaria Journal, vol. 17, no. 1, pp. 1-11, 2018.

[27] Wood B., Bambery K., Dixon M., Tilley L., Malaria Parasite Detection on Microscopic Blood Smear Images with Integrated ... 179 Nasse M., Mattsone E., and Hirschmugl C., “Diagnosing Malaria Infected Cells at The Single Cell Level Using Focal Plane Array Fourier Transform Infrared Imaging Spectroscopy,” The Analyst, vol. 139, no. 19, pp. 4769-4774, 2014.

[28] World Health Organization, World Malaria Report, stories/detail/world-malaria-report-2019, Last Visited, 2019.

[29] Yang F., Poostchi M., Yu H., Zhou Z., Silamut K., Yu J., Maude J., Jaeger S., and Antani S., “Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1427-1438, 2020.

[30] Yu H., Yang F., Rajarama S., Ersoy I., Moallem G., Poostchi M., Palaniappan K., Antani S., Maude R., and Jaeger S., “Malaria Screener: A Smartphone Application for Automated Malaria Screening,” BMC Infectious Diseases, vol. 20, no. 1, pp. 1-8, 2020.

[31] Yuan Y. and Meng M., “Deep Learning for Polyp Recognition in Wireless Capsule Endoscopy Images,” Medical Physics, vol. 44, no. 4, pp. 1379-1389, 2017.