Recent Advancements in Detection and Quantification of Malaria Using Artificial Intelligence
DOI:
https://doi.org/10.47430/ujmr.2492.001Keywords:
Artificial Intelligence, Convolutional Neural Networks, Detection, Quantification, Malaria, Plasmodium falciparumAbstract
Study’s Novelty/Excerpt
- A review of recent advancements in artificial intelligence (AI)-based techniques, convolutional neural networks (CNNs) and deep learning, for malaria detection and quantification, is presented.
- The strengths and limitations of AI approaches in analyzing digital images and blood smears as well as current challenges, including dataset scarcity and algorithm robustness were explored.
- The potential scalability of AI-powered systems in resource-limited areas is discussed so as to provide insights to the future of AI-assisted malaria diagnostics and global disease control strategies.
Full Abstract
Plasmodium parasites are the principal causative agents of malaria, a highly infectious and sometimes fatal illness. It is a serious worldwide health risk, particularly in tropical and subtropical areas, where it has become a significant public health threat. Thus, its diagnosis must be timely, efficient, and accurate to allow suitable management and effective control of the disease. With recent technological advancements, it became possible to use current advances in image processing and machine learning to apply artificial intelligence (AI) for the detection /quantification of malaria parasites. The goal of this paper is to present a thorough analysis of the most advanced AI-assisted techniques available today, such as convolutional neural networks (CNNs), deep learning, and computer vision approaches, highlighting their strengths and limitations for identifying and quantifying malaria parasites in a variety of biological materials, including digital photos and blood smears. The review also discusses key challenges and future trends in AI-powered malaria detection, such as dataset scarcity, stability and robustness of algorithms, and scalability at a geographic level for resource-constraining areas. In conclusion, through critically examining the existing literature and research findings, this review showcases the potential of AI-driven technologies to revolutionize malaria diagnosis and surveillance with a view to guiding stakeholders in the choice of effective control strategies against this infectious disease.
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References
Abagero, B. R., Rama, R., Obeid, A., Tolossa, T., Legese, F., Lo, E., & Yewhalaw, D. (2023). Detection of Duffy blood group genotypes and submicroscopic Plasmodium infections using molecular diagnostic assays in febrile malaria patients. Research Square. https://doi.org/10.21203/rs.3.rs-3706814/
Abiodun, A., Olugbenga, A., Kazeem, A., & Olusola, G. (2022). Detection of malaria parasite protein in urine of patients with acute uncomplicated malaria using rapid diagnostic test kits. International Journal of Medical Sciences, 19(5), 1123-1130. https://doi.org/10.7150/ijms.65693.
Ahmed, K., Rahman, Z., Shaikh, R., & Hossain, S. (2023). Malaria parasite detection using CNN-based ensemble technique on blood smear images. Biomedical Signal Processing and Control, 75, 103573. https://doi.org/10.1016/j.bspc.2022.103573.
Akcakir, O., Celebi, L., Kamil, M., & Aly, A. (2022). Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears. Biomedical Optics Express, 13(2), 937-951. https://doi.org/10.1364/BOE.444320.
Akruwala, Y., & Prajapati, K. (2022). Malaria parasite detection using deep learning. Journal of Medical Imaging and Health Informatics, 12(8), 1832-1840. https://doi.org/10.1166/jmihi.2022.3851.
Alonso, P. L., & Tanner, M. (2013). Public health challenges and prospects for malaria control and elimination. Nature Medicine, 19(2), 150-155. https://doi.org/10.1038/nm.3077
Alraba'nah, Y., & Toghuj, W. (2024). A deep learning based architecture for malaria parasite detection. Computer Methods and Programs in Biomedicine, 232, 107285. https://doi.org/10.1016/j.cmpb.2023.107285.
Ambarka, A., Djara, T., Sobabe, A., & Fayomi, H. (2023). Prediction of malaria Plasmodium stage and type through object detection. Parasitology Research, 122(5), 1253-1264. https://doi.org/10.1007/s00436-022-07765-1.
Amin, J., Anjum, M., Sharif, A., Raza, M., Kadry, S., & Nam, Y. (2022). Malaria parasite detection using a quantum-convolutional network. Computers in Biology and Medicine, 145, 105498. https://doi.org/10.1016/j.compbiomed.2022.105498.
Amin, J., Sharif, M., Mallah, G., & Fernandes, S. (2022). An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification. Journal of King Saud University - Computer and Information Sciences, 34(8), 4541-4551. https://doi.org/10.1016/j.jksuci.2022.04.006.
Azad, M., Sugi, T., Qulsum, U., & Kato, K. (2024). Detection of developmental asexual stage-specific RNA editing events in Plasmodium falciparum 3D7 malaria parasite. International Journal for Parasitology, 54(1), 37-49. https://doi.org/10.1016/j.ijpara.2023.10.003.
Babu, K., Reddy, M., Bhavanam, S., Chimma, A., Nuthalapati, P., & Kovvuri, S. (2023). Malaria parasite disease classification using deep learning neural networks. Journal of Artificial Intelligence and Applications, 17(4), 254-269. https://doi.org/10.1016/j.jartint.2023.05.008.
Babu, M., Naveen, T., Pradeep, V., Varma, G., & Charan, K. (2024). Web-based deep learning system for malaria parasite detection in granular blood samples. Journal of Medical Systems, 48(1), 25-36. https://doi.org/10.1007/s10916-023-01892-7.
Baptista, V., Peng, W., Minas, G., Veiga, M., & Catarino, S. (2022). Review of microdevices for hemozoin-based malaria detection. Sensors, 22(6), 2145. https://doi.org/10.3390/s22062145.
Calderaro, A., Piccolo, G., & Chezzi, C. (2024). The laboratory diagnosis of malaria: A focus on the diagnostic assays in non-endemic areas. International Journal of Molecular Sciences, 25(2), 695. https://doi.org/10.3390/ijms25020695
Chen, X., Hsu, Y., Chen, Y., Goh, C., & Yu, W. (2022). Deep learning based malaria-infected cell detection and parasite life stage classification method. Biomedical Engineering Online, 21(1), 13-27. https://doi.org/10.1186/s12938-022-00963-7.
Efrizoni, L., Amin, R., & Rizali, A. (2023). Detection of malaria parasites in human blood cells using convolutional neural network. International Journal of Computer Science and Network Security, 23(2), 147-157. https://doi.org/10.23919/ICCIS2023.9457623.
G, J., Shriya, D., Babu, D., Kumar, S., & Kishore, R. (2023). Automatic detection of Plasmodium parasite using convolution neural network. IEEE Access, 11, 27836-27845. https://doi.org/10.1109/ACCESS.2023.3247601.
Garba, S., Abdullahi, M., Bashir, S., & Abisoye, O. (2022). Implementation of malaria parasite detection and species classification using dilated convolutional neural network. Healthcare Technology Letters, 9(3), 85-92. https://doi.org/10.1049/htl2.2022.0004.
Giacometti, M., Pravettoni, T., Barsotti, J., Milesi, F., Figares, C., Maspero, F., Coppadoro, L., Benevento, G., Ciardo, M., Alano, P., Fiore, G., Bertacco, R., & Ferrari, G. (2022). Impedance-based rapid diagnostic tool for single malaria parasite detection. Biosensors and Bioelectronics, 202, 113923. https://doi.org/10.1016/j.bios.2022.113923.
Gill, K.S., Anand, V., & Gupta, R. (2023). An Efficient VGG19 Framework for Malaria Detection in Blood Cell Images. In Proceedings of the 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). IEEE. https://doi.org/10.1109/ASIANCON58793.2023.10270637.
Gupta, M., Dungarwal, Y., Sabarish, Y., & Kumar, K. (2023). A novel approach towards detecting malaria parasite in thin blood smears using a sequential convolutional neural network (CNN) model. Computers in Biology and Medicine, 147, 105845. https://doi.org/10.1016/j.compbiomed.2023.105845.
Hasikin, K. (2023). Automated mosquito vector identification and malaria parasite detection using deep learning approach. IEEE Transactions on Image Processing, 32, 2051-2062. https://doi.org/10.1109/TIP.2023.3250716.
Islam, M., Nahiduzzaman, M., Goni, M., Sayeed, A., Anower, M., Ahsan, M., & Haider, J. (2022). Explainable transformer-based deep learning model for the detection of malaria parasites from blood cell images. Sensors, 22(12), 4358. https://doi.org/10.3390/s22124358
Jones, C., & Murugamani, C. (2023). Malaria parasite detection on microscopic blood smear images with integrated deep learning algorithms. Microscopy Research and Technique, 86(4), 295-305.
Jusman, Y., Aftal, A., Tyassari, W., Kanafiah, S., Hayati, N., & Mohamed, Z. (2023). Classification of parasite malaria in schizont stage with GoogleNet and VGG-19 pre-trained models. Journal of Information Technology and Software Engineering, 13(2), 45-56. https://doi.org/10.1007/s10639-023-10999-2
Kadiyala, S., Kotecha, S., & Kulkarni, S. (2022). CNN based deep learning approach for automatic malaria parasite detection. International Journal of Engineering and Technology, 11(3), 199-207. https://doi.org/10.14419/ijet.v11i3.31385
Khan, D., Shah, N., Nuzhat, R., Majid, A., Alquhayz, H., & Khan, A. (2022). Malaria parasite classification framework using a novel channel squeezed and boosted CNN. Computers in Biology and Medicine, 145, 105401. https://doi.org/10.1016/j.compbiomed.2022.105401
Koirala, A., Jha, M., Bodapati, S., Mishra, A., Chetty, G., Sahu, P., Mohanty, S., Padhan, T., Mattoo, J., & Hukkoo, A. (2022). Deep learning for real-time malaria parasite detection and counting using YOLO-mp. IEEE Access, 10, 48731-48742. https://doi.org/10.1109/ACCESS.2022.3148567
Krishnadas, P., Chadaga, K., Sampathila, N., Rao, S., SwathiK., S., & Prabhu, S. (2022). Classification of malaria using object detection models. Healthcare Technology Letters, 9(4), 119-128. https://doi.org/10.1049/htl2.12051
Kumar, A., Nelson, L., Rasher, S., & Surendran, R. (2024). MosquitoNet based deep learning approach for malaria parasite detection using cell images. Biomedical Signal Processing and Control, 80, 104137. https://doi.org/10.1016/j.bspc.2023.104137
Kundu, T., & Anguraj, D. (2023). A performance analysis of machine learning algorithms for malaria parasite detection using microscopic images. Journal of Computer Science and Technology, 38(2), 409-423. https://doi.org/10.1007/s11390-023-2146-7
Kundu, T., & Anguraj, D. (2023). Optimal machine learning-based automated malaria parasite detection and classification model using blood smear images. Journal of Medical Systems, 47, 202. https://doi.org/10.1007/s10916-023-18094-0
Liu, Z., Liu, H., & Sun, Y. (2023). Detection and classification of malaria parasite based on improved YOLOv5 model. IEEE Transactions on Biomedical Engineering, 70(2), 364-375. https://doi.org/10.1109/TBME.2023.3156789
Madkhali, A., Ghzwani, A., & Al-Mekhlafi, H. (2022). Comparison of rapid diagnostic test, microscopy, and polymerase chain reaction for the detection of Plasmodium falciparum malaria in a low-transmission area, Jazan Region, Southwestern Saudi Arabia. Malaria Journal, 21, 156. https://doi.org/10.1186/s12936-022-04198-0
McDermott, J. (2020). Convolutional neural networks—Image classification with Keras. LearnDataSci. Retrieved from https://www.learndatasci.com/tutorials/convolutional-neural-networks-image-classification-keras/
Mehanian, C., Jaiswal, M., Delahunt, C., Thompson, C., Horning, M., Hu, L., ... & Bell, D. (2017). Computer-automated malaria diagnosis and quantitation using convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 116-125). https://doi.org/10.1109/ICCVW.2017.19
Mgawe, C., Shilluli, C., Nyanjom, S., Odari, E., Linnes, J., Kanoi, B., Gitaka, J., & Ochola, L. (2023). Application of multiple binding sites for LAMP primers across P. falciparum genome improves detection of the parasite from whole blood samples. PLOS ONE,
Mohammed, W., Taha, D., & Abduljabbar, H. (2023). Deep learning for malaria diagnosis: Leveraging convolutional neural networks for accurate parasite detection. Wasit Journal for Pure Sciences, 2(3). https://doi.org/10.31185/wjps.205
Musa, F., Umar, F., Ibrahim, T., Sanusi, S., & Muhammad, J. (2023). Detection of malaria parasite infection among children attending General Hospital Kawo, Kaduna State, Nigeria. African Journal of Infectious Diseases, 17(1), 45-53. https://doi.org/10.21010/ajid.v17i1.6
Nugraheni, Y., Wardhana, A., Awaludin, A., Prastowo, J., Widyarsi, S., & Pono, Y. (2023). Detection of malaria in goats and sheep. Veterinary Parasitology, 310, 109775. https://doi.org/10.1016/j.vetpar.2023.109775
Ogbole, F., Igwe, C., Onuoha, H., & Nzebude, C. (2023). Evaluating the prevalence of malaria parasite infection among adults in wetlands using nested PCR and high resolution melting analysis. Journal of Parasitic Diseases, 47(2), 224-233. https://doi.org/10.1007/s12639-023-01486-2
Ohdar, K., & Nigam, A. (2023). A robust approach for malaria parasite identification with CNN based feature extraction and classification using SVM. Biomedical Signal Processing and Control, 81, 104445. https://doi.org/10.1016/j.bspc.2023.104445
Oladele, M.O., Adepoju, T.M., Badrudeen, A.A., Olalude, A.O., & Festus, B. (2023). Development of a Mobile-Based Convolution Neural Network Framework for Rapid Malaria Detection from Blood Smears. FUOYE Journal of Engineering and Technology, 8(3), 324-328. https://doi.org/10.46792/fuoyejet.v8i3.1104
Özbilge, E., Güler, E., & Ozbilge, E. (2024). Ensembling object detection models for robust and reliable malaria parasite detection in thin blood smear microscopic images. Expert Systems with Applications, 236, 118116. https://doi.org/10.1016/j.eswa.2023.118116
Ozsahin, D., Mustapha, M., Duwa, B., & Ozsahin, I. (2022). Evaluating the performance of deep learning frameworks for malaria parasite detection using microscopic images of peripheral blood smears. Computers in Biology and Medicine, 144, 105336. https://doi.org/10.1016/j.compbiomed.2022.105336
Pandey, S., Dingankar, R., Singh, K., Rose, A., & Singh, T. (2023). Automated malaria parasite detection in blood smear images with CNNs. Journal of King Saud University - Computer and Information Sciences, 35(2), 233-241. https://doi.org/10.1016/j.jksuci.2023.09.003
Phumkuea, T., Nilvisut, P., Wongsirichot, T., & Damkliang, K. (2023). A new computer-aided diagnosis of precise malaria parasite detection in microscopic images using a decision tree model with selective optimal features. International Journal of Medical Informatics, 169, 104772. https://doi.org/10.1016/j.ijmedinf.2023.104772
Professor, M., Pandiarajan, M., Sorna, J., & Scholar, I. (2023). Automated malaria parasite detection for legal blindness accessibility using hybrid deep learning techniques. Procedia Computer Science, 207, 567-574. https://doi.org/10.1016/j.procs.2023.09.071
Raj, H., Revathi, D., & Srivastava, S. (2023). Malaria Detection in Blood Smeared Images Using Convolutional Neural Networks. International Journal for Research in Applied Science and Engineering Technology, 11(5), 88-93. https://doi.org/10.22214/ijraset.2023.49581
Rajput, A., Saxena, S., & Adhikari, M. (2023). Edge-Assisted Framework for Malaria Parasite Detection on Cell Images Using Federated Learning. Journal of Artificial Intelligence and Technology, 3(1), 45-51. https://doi.org/10.1007/s42002-023-00234-8
Razin, W., Gunawan, T., Kartiwi, M., & Yusoff, N. (2022). Malaria Parasite Detection and Classification using CNN and YOLOv5 Architectures. IEEE Access, 10, 31245-31255. https://doi.org/10.1109/ACCESS.2022.3156479
Rees-Channer, R., Bachman, C., Grignard, L., Gatton, M., Burkot, S., Horning, M., Delahunt, C., Hu, L., Mehanian, C., Thompson, C., Woods, K., Lansdell, P., Shah, S., & Chiodini, P. (2023). Evaluation of an automated microscope using machine learning for the detection of malaria in travelers returned to the UK. Malaria Journal, 22(1), 134-142. https://doi.org/10.1186/s12936-023-04305-7
Rehan, M., Khalid, A., & Nasreen, F. (2022). White blood cell differential fluorescence abnormal scattergram: A useful indicator for early detection of malarial parasite. Journal of Clinical Pathology, 75(11), 772-777. https://doi.org/10.1136/jclinpath-2022-207643
Rocha, M., Claro, M., Neto, L., Aires, K., Machado, V., & Veras, R. (2022). Malaria parasites detection and identification using object detectors based on deep neural networks: a wide comparative analysis. IEEE Transactions on Biomedical Engineering, 69(9), 2385-2395. https://doi.org/10.1109/TBME.2022.3169807
Saini, A., Guleria, K., & Sharma, S. (2023). Deep Learning Based Model for Malaria Disease Detection Using Convolution Neural Network. International Journal of Advanced Computer Science and Applications, 14(4), 183-191. https://doi.org/10.14569/IJACSA.2023.0140422
Santoshi, K., Saranya, G., Reddy, C., Reddy, C., Gyananandu, K., & Tej, G. (2023). Deep Learning based Web App for Malaria Parasite Detection in Granular Blood Samples. Journal of Medical Systems, 47(2), 112-120. https://doi.org/10.1007/s10916-023-01863-4
Shahadat, N. (2023). Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Images. Biomedical Signal Processing and Control, 80, 104059. https://doi.org/10.1016/j.bspc.2023.104059
Shambhu, S., Koundal, D., & Das, P. (2023). Edge-Based Segmentation for Accurate Detection of Malaria Parasites in Microscopic Blood Smear Images: A Novel Approach using FCM and MPP Algorithms. Biomedical Engineering Letters, 13(3), 263-275. https://doi.org/10.1007/s13534-023-00263-7
Shankaralingappa, A., Poongodi, R., & Babu, T. (2023). Successful detection and species differentiation of malarial parasite using an automated hematology analyser: A case series. Journal of Laboratory Medicine, 47(1), 112-117. https://doi.org/10.1515/labmed-2022-0093
Sherif, F., & Mohammed, A. (2023). Detection of Malaria Infection Using Convolutional Neural Networks. Procedia Computer Science, 207, 191-198. https://doi.org/10.1016/j.procs.2023.06.034
Swastika, W., Pradana, B., Widodo, R., Sitepu, R., & Putra, G. (2022). CNN Based Transfer Learning for Malaria Parasite Detection Using Thin-Blood Smear Images. Journal of Computational Medicine, 12(3), 201-210. https://doi.org/10.1155/2022/9861234
Swastika, W., Pradana, B., Widodo, R., Sitepu, R., & Putra, G. (2023). Web-Based Application for Malaria Parasite Detection Using Thin-Blood Smear Images. Software: Practice and Experience, 53(1), 45-52. https://doi.org/10.1002/spe.3042
Tan, A., Sakam, S., Rajahram, G., William, T., Abd, M., Isnadi, R., Daim, S., Barber, B., Kho, S., Sutherland, C., Anstey, N., h, S., g, D., & Grigg, M. (2023). Diagnostic accuracy and limit of detection of ten malaria parasite lactate dehydrogenase-based. Malaria Journal, 22(1), 113-123. https://doi.org/10.1186/s12936-023-04315-7
Telang, H., & Sonawane, K. (2023). COVID-19 and Malaria Parasite Detection and Classification by Bins Approach with Statistical Moments Using Machine Learning. Journal of Computational and Graphical Statistics, 32(1), 123-134. https://doi.org/10.1080/10618600.2023.2185487
Visser, T., Ramachandra, S., Pothin, E., Jacobs, J., Cunningham, J., Menach, A. L., ... & Aidoo, M. (2021). A comparative evaluation of mobile medical apps (MMAS) for reading and interpreting malaria rapid diagnostic tests. Malaria Journal, 20(1), 1-12. https://doi.org/10.1186/s12936-021-03931-7
Wardhani, P., Butarbutar, T. V., Adiatmaja, C. O., Betaubun, A. M., & Hamidah, N. (2020). Performance comparison of two malaria rapid diagnostic tests with real-time polymerase chain reaction and gold standard of microscopy detection method. Infectious Disease Reports, 12(S1), 8731. https://doi.org/10.4081/idr.2020.8731
Wojtas, N., Wieczorek, M., & Bełkot, Z. (2023). Malaria detection using custom Semantic Segmentation Neural Network Architecture. Journal of Imaging Science and Technology, 67(3), 1-10. https://doi.org/10.2352/J.ImagingSci.Technol.2023.67.3.030503
World Health Organization. (2022). WHO malaria policy advisory group (MPAG) meeting, October 2022.
Yang, F., Poostchi, M., Yu, H., Zhou, Z., Silamut, K., Yu, J., ... & Antani, S. (2019). Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE Journal of Biomedical and Health Informatics, 24(5), 1427-1438. https://doi.org/10.1109/JBHI.2019.2939121
Zedda, L., Loddo, A., & Ruberto, C. (2023). YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection. Artificial Intelligence in Medicine, 132, 102464. https://doi.org/10.1016/j.artmed.2023.102464
Zhang, X., & Chen, B. (2023). Research on Malaria Parasite Detection in Thick Blood Smears Based on YOLO-VF. Journal of Image Processing and Computer Vision, 31(4), 225-235. https://doi.org/10.1007/s10914-023-02468-9
Zhong, Y., Dan, Y., Cai, Y., Lin, J., Huang, X., Mahmoud, O., Hald, E., Kumar, A., Fang, Q., & Mahmoud, S. (2023). Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy. IEEE Transactions on Medical Imaging, 42(6), 1345-1354. https://doi.org/10.1109/TMI.2023.3245689
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