Abstract
Malaria is a globally widespread disease caused by parasitic protozoa transmitted by infected female Anopheles mosquitoes. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analyzing digital microscopic blood smears, which is tedious, time-consuming, and error-prone. Therefore, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work proposes a real-time malaria parasite detector and classification system after studying the YOLOv5 detector and comparing different off-the-shelf convolutional neural network architectures for four-class classification on Plasmodium Falciparum life stages. The results show that the use of the networks YOLOv5 and DarkNet-53 reaches great accuracy in detecting and classifying the life stages of Plasmodium Falciparum, achieving an accuracy of 95.2% and 96.02%, respectively, and outperforming the state-of-art. The obtained results enable broad improvements geared explicitly towards recognizing types and life stages of less common species of malaria parasites, even in mobile environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Available at: https://github.com/ultralytics/yolov5.
References
Abdurahman, F., Fante, K.A., Aliy, M.: Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models. BMC Bioinform. 22(1), 112 (2021)
Jocher, G., et al.: ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, April 2021. https://doi.org/10.5281/zenodo.4679653
Bias, S., Reni, S., Kale, I.: Mobile hardware based implementation of a novel, efficient, fuzzy logic inspired edge detection technique for analysis of malaria infected microscopic thin blood images. Proc. Comput. Sci. 141, 374–381 (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database In: CVPR, pp. 248–255 (2009)
Di Ruberto, C., Loddo, A., Puglisi, G.: Blob detection and deep learning for leukemic blood image analysis. Appli. Sci. 10(3), 1176 (2020)
Di Ruberto, C., Loddo, A., Putzu, L.: Detection of red and white blood cells from microscopic blood images using a region proposal approach. Comput. Biol. Med. 116, 103530 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 770–778 (2016)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceeding of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097–1105 (2012)
Liang, Z., et al.: CNN-based image analysis for malaria diagnosis. In: IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15–18 December, pp. 493–496. IEEE Computer Society (2016)
Lin, T.Y., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision - ECCV 2014, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Loddo, A., Di Ruberto, C., Kocher, M.: Recent advances of malaria parasites detection systems based on mathematical morphology. Sensors 18(2), 513 (2018)
Loddo, A., Di Ruberto, C., Kocher, M., Prod’Hom, G.: MP-IDB: the malaria parasite image database for image processing and analysis. In: Lepore, N., Brieva, J., Romero, E., Racoceanu, D., Joskowicz, L. (eds.) SaMBa 2018. LNCS, vol. 11379, pp. 57–65. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13835-6_7
Maity, M., Jaiswal, A., Gantait, K., Chatterjee, J., Mukherjee, A.: Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet. Pattern Recognit. Lett. 138, 88–94 (2020)
Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop, IIPhDW, pp. 117–122 (2018)
Moody, A.: Rapid diagnostic tests for malaria parasites. Clin. Microbiol. Rev. 15(1), 66–78 (2002)
Nanni, L., Ghidoni, S., Brahnam, S.: Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recogn. 71, 158–172 (2017)
World Health Organization. https://www.who.int/news-room/fact-sheets/detail/malaria (2021). Accessed 13 Sept 2021
Poostchi, M., Silamut, K., Maude, R.J., Jaeger, S., Thoma, G.: Image analysis and machine learning for detecting malaria. Transl. Res. 194, 36–55 (2018); in-Depth Review: Diagnostic Medical Imaging
Rahman, A., Zunair, H., Reme, T.R., Rahman, M.S., Mahdy, M.: A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset. Tissue Cell 69, 101473 (2021)
Rajaraman, S., Jaeger, S., Antani, S.K.: Perf. eval. of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7, e6977 (2019)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. CoRR abs/1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June, pp. 4510–4520. IEEE Computer Society (2018)
Shin, H., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May, Conference Track Proceedings (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–9 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2818–2826 (2016)
Vogado, L.H., Veras, R.M., Araujo, F.H., Silva, R.R., Aires, K.R.: Leukemia diagnosis in blood slides using transfer learning in CNNS and SVM for classification. Eng. Appl. Artif. Intell. 72, 415–422 (2018)
Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June, pp. 6848–6856. IEEE Computer Society (2018)
Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV Workshops, pp. 2778–2788, October 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zedda, L., Loddo, A., Di Ruberto, C. (2022). A Deep Learning Based Framework for Malaria Diagnosis on High Variation Data Set. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-06430-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06429-6
Online ISBN: 978-3-031-06430-2
eBook Packages: Computer ScienceComputer Science (R0)