Research Article
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Year 2022, Issue: 048, 42 - 54, 31.03.2022

Abstract

References

  • [1] Vijayalakshmi, A. And Rajesh, K.B., (2020), Deep learning approach to detect malaria from microscopic images, Multimedia Tools and Applications, 79(21), 15297-15317.
  • [2] Yang, F., Poostchi, M., Yu, H., Zhou, Z., Silamut, K., Yu, J., Maude, R.J., Jaeger, S. and 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.
  • [3] Diker, A.D., (2020), Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(4), 1825-1835.
  • [4] Li, S., Du, Z., Meng, X. and Zhang, Y., (2021), Multi-stage malaria parasite recognition by deep learning, GigaScience, 10(6), giab040.
  • [5] Akılotu, B.N., Kadiroğlu, Z., Şengür, A. and Kayaoğlu, M., Evrişimsel Sinir Ağları ve Transfer Öğrenme Yöntemi Kullanılarak Sıtma Tespiti.
  • [6] Rahman, A., Zunair, H., Reme, T.R., Rahman, M.S. and Mahdy, M.R.C., (2021), A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset, Tissue and Cell, 69, 101473.
  • [7] Chakradeo, K., Delves, M. and Titarenko, S., (2021), Malaria Parasite Detection Using Deep Learning Methods, International Journal of Computer and Information Engineering, 15(2), 175-182.
  • [8] Swastika, W., Widodo, R.B., Balqis, G.A. and Sitepu, R., (2021), The Effect of Regularization on Deep Learning Methods For Detection of Malaria Infection, In 2021 International Conference on Converging Technology in Electrical and Information Engineering (ICCTEIE), Bandar Lampung, 87-90.
  • [9] Yang, Z., Benhabiles, H., Hammoudi, K., Windal, F., He, R. and Collard, D., (2021), A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images, Neural Computing and Applications, 1-16.
  • [10] Swastika, W., Kristianti, G.M. and Widodo, R.B., (2021), Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning, In Journal of Physics: Conference Series, Malang, 1869(1), 012092.
  • [11] Imran, T., Sharif, M., Tariq, U., Zhang, Y.D., Nam, Y., Nam, Y. and Kang, B.G., (2021), Malaria Blood Smear Classification Using Deep Learning and Best Features Selection, Comput. Mater. Contin, 71, 1-15.
  • [12] Raj, M., Sharma, R. and Sain, D., (2021), A Deep Convolutional Neural Network for Detection of Malaria Parasite in Thin Blood Smear Images, In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, 510-514.
  • [13] Montalbo, F.J.P. and Alon, A.S., (2021), Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears, KSII Transactions on Internet and Information Systems (TIIS), 15(1), 147-165.
  • [14] Fuhad, K.M., Tuba, J.F., Sarker, M., Ali, R., Momen, S., Mohammed, N. and Rahman, T., (2020), Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application, Diagnostics, 10(5), 329.
  • [15] Masud, M., Alhumyani, H., Alshamrani, S.S., Cheikhrouhou, O., Ibrahim, S., Muhammad, G., Hossain, M.S. and Shorfuzzaman, M., (2020) Leveraging deep learning techniques for malaria parasite detection using mobile application, Wireless Communications and Mobile Computing, 2020, 1-15.
  • [16] Pattanaik, P.A., Mittal, M. and Khan, M.Z., (2020), Unsupervised deep learning cad scheme for the detection of malaria in blood smear microscopic images, IEEE, 8, 94936-94946.
  • [17] Sriporn, K., Tsai, C.F., Tsai, C.E. and Wang, P., (2020), Analyzing Malaria Disease Using Effective Deep Learning Approach, Diagnostics, 10(10), 744.
  • [18] Shah, D., Kawale, K., Shah, M., Randive, S. and Mapari, R., (2020), Malaria Parasite Detection Using Deep Learning:(Beneficial to humankind), In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 984-988.
  • [19] Nakasi, R., Mwebaze, E., Zawedde, A., Tusubira, J., Akera, B. and Maiga, G., (2020), A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models, SN Applied Sciences, 2(7), 1-7.
  • [20] Sinha, S., Srivastava, U., Dhiman, V., Akhilan, P.S. and Mishra, S., (2021), Performance assessment of Deep Learning procedures on Malaria dataset, Journal of Robotics and Control (JRC), 2(1), 12-18.
  • [21] https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#malaria-datasets
  • [22] He, K., Zhang, X., Ren, S. and Sun, J., (2016), Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.1109/CVPR.2016.90.
  • [23] Wu, H., Xin, M., Fang, W., Hu, H.M. and Hu, Z., (2019), Multi-level feature network with multi-loss for person re-identification, IEEE, 7, 91052-91062.

CLASSIFICATION of CELLS INFECTED with the MALARIA PARASITE with RESNET ARCHITECTURES

Year 2022, Issue: 048, 42 - 54, 31.03.2022

Abstract

Malaria is a disease that causes a parasite called plasmodium to be transmitted to humans as a result of the bite of female anopheles’ mosquitoes. Malaria is detected by examining the blood sample taken from the patient as a result of a microbiological examination under a microscope by specialist physicians. Although microscopy is widely used, its efficiency is low because it is time-consuming and depends on the interpretation of the specialist physician. In recent years, deep learning methods used in the field of computer vision increase the efficiency of specialist physicians by making a significant contribution to the decision-making process in solving real-life problems. In this study, ResNet architectures were preferred to quickly classify the malaria parasite using deep learning methods. For the training and testing of ResNet architectures, a dataset consisting of a total of 27558 red blood cell images containing 13779 parasitized and 13779 uninfected were used. Using this dataset, ResNet architectures were compared. As a result of the comparison, the best success accuracy (94.09%) was obtained with the ResNet-50 v2 model.

References

  • [1] Vijayalakshmi, A. And Rajesh, K.B., (2020), Deep learning approach to detect malaria from microscopic images, Multimedia Tools and Applications, 79(21), 15297-15317.
  • [2] Yang, F., Poostchi, M., Yu, H., Zhou, Z., Silamut, K., Yu, J., Maude, R.J., Jaeger, S. and 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.
  • [3] Diker, A.D., (2020), Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(4), 1825-1835.
  • [4] Li, S., Du, Z., Meng, X. and Zhang, Y., (2021), Multi-stage malaria parasite recognition by deep learning, GigaScience, 10(6), giab040.
  • [5] Akılotu, B.N., Kadiroğlu, Z., Şengür, A. and Kayaoğlu, M., Evrişimsel Sinir Ağları ve Transfer Öğrenme Yöntemi Kullanılarak Sıtma Tespiti.
  • [6] Rahman, A., Zunair, H., Reme, T.R., Rahman, M.S. and Mahdy, M.R.C., (2021), A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset, Tissue and Cell, 69, 101473.
  • [7] Chakradeo, K., Delves, M. and Titarenko, S., (2021), Malaria Parasite Detection Using Deep Learning Methods, International Journal of Computer and Information Engineering, 15(2), 175-182.
  • [8] Swastika, W., Widodo, R.B., Balqis, G.A. and Sitepu, R., (2021), The Effect of Regularization on Deep Learning Methods For Detection of Malaria Infection, In 2021 International Conference on Converging Technology in Electrical and Information Engineering (ICCTEIE), Bandar Lampung, 87-90.
  • [9] Yang, Z., Benhabiles, H., Hammoudi, K., Windal, F., He, R. and Collard, D., (2021), A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images, Neural Computing and Applications, 1-16.
  • [10] Swastika, W., Kristianti, G.M. and Widodo, R.B., (2021), Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning, In Journal of Physics: Conference Series, Malang, 1869(1), 012092.
  • [11] Imran, T., Sharif, M., Tariq, U., Zhang, Y.D., Nam, Y., Nam, Y. and Kang, B.G., (2021), Malaria Blood Smear Classification Using Deep Learning and Best Features Selection, Comput. Mater. Contin, 71, 1-15.
  • [12] Raj, M., Sharma, R. and Sain, D., (2021), A Deep Convolutional Neural Network for Detection of Malaria Parasite in Thin Blood Smear Images, In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, 510-514.
  • [13] Montalbo, F.J.P. and Alon, A.S., (2021), Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears, KSII Transactions on Internet and Information Systems (TIIS), 15(1), 147-165.
  • [14] Fuhad, K.M., Tuba, J.F., Sarker, M., Ali, R., Momen, S., Mohammed, N. and Rahman, T., (2020), Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application, Diagnostics, 10(5), 329.
  • [15] Masud, M., Alhumyani, H., Alshamrani, S.S., Cheikhrouhou, O., Ibrahim, S., Muhammad, G., Hossain, M.S. and Shorfuzzaman, M., (2020) Leveraging deep learning techniques for malaria parasite detection using mobile application, Wireless Communications and Mobile Computing, 2020, 1-15.
  • [16] Pattanaik, P.A., Mittal, M. and Khan, M.Z., (2020), Unsupervised deep learning cad scheme for the detection of malaria in blood smear microscopic images, IEEE, 8, 94936-94946.
  • [17] Sriporn, K., Tsai, C.F., Tsai, C.E. and Wang, P., (2020), Analyzing Malaria Disease Using Effective Deep Learning Approach, Diagnostics, 10(10), 744.
  • [18] Shah, D., Kawale, K., Shah, M., Randive, S. and Mapari, R., (2020), Malaria Parasite Detection Using Deep Learning:(Beneficial to humankind), In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 984-988.
  • [19] Nakasi, R., Mwebaze, E., Zawedde, A., Tusubira, J., Akera, B. and Maiga, G., (2020), A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models, SN Applied Sciences, 2(7), 1-7.
  • [20] Sinha, S., Srivastava, U., Dhiman, V., Akhilan, P.S. and Mishra, S., (2021), Performance assessment of Deep Learning procedures on Malaria dataset, Journal of Robotics and Control (JRC), 2(1), 12-18.
  • [21] https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#malaria-datasets
  • [22] He, K., Zhang, X., Ren, S. and Sun, J., (2016), Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.1109/CVPR.2016.90.
  • [23] Wu, H., Xin, M., Fang, W., Hu, H.M. and Hu, Z., (2019), Multi-level feature network with multi-loss for person re-identification, IEEE, 7, 91052-91062.
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İsmail Akgül 0000-0003-2689-8675

Volkan Kaya 0000-0001-6940-3260

Publication Date March 31, 2022
Submission Date February 3, 2022
Published in Issue Year 2022 Issue: 048

Cite

IEEE İ. Akgül and V. Kaya, “CLASSIFICATION of CELLS INFECTED with the MALARIA PARASITE with RESNET ARCHITECTURES”, JSR-A, no. 048, pp. 42–54, March 2022.