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A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease

Yıl 2022, Cilt: 5 Sayı: 3, 427 - 447, 31.12.2022

Emel Soylu

https://doi.org/10.35377/saucis...1197119
Cited By: 1

Öz

Malaria is a disease caused by a parasite. The parasite is transmitted to humans through the bite of infected mosquitoes. Thousands of people die every year due to malaria. When this disease is diagnosed early, it can be fully treated with medication. Diagnosis of malaria can be made according to the presence of parasites in the blood taken from the patient. In this study, malaria detection and diagnosis study were performed using The Malaria dataset containing a total of 27,558 cell images with samples of equally parasitized and uninfected cells from thin blood smear slide images of segmented cells. It is possible to detect malaria from microscopic blood smear images via modern deep learning techniques. In this study, 5 of the popular convolutional neural network architectures for malaria detection from cell images were retrained to find the best combination of architecture and learning algorithm. AlexNet, GoogLeNet, ResNet-50, MobileNet-v2, VGG-16 architectures from pre-trained networks were used, their hyperparameters were adjusted and their performances were compared. In this study, a maximum 96.53% accuracy rate was achieved with MobileNet-v2 architecture using the adam learning algorithm

Anahtar Kelimeler

malaria detection deep transfer learning Matlab Convolutional Neural Network

Kaynakça

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  • [6] A. Uysal, S. Gokay, E. Soylu, T. Soylu, and S. Çaşka, “Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication,” Meas. Control (United Kingdom), vol. 52, no. 7–8, 2019.
  • [7] L. V. Selby, W. R. Narain, A. Russo, V. E. Strong, and P. Stetson, “Autonomous detection, grading, and reporting of postoperative complications using natural language processing,” Surg. (United States), vol. 164, no. 6, pp. 1300–1305, 2018.
  • [8] A. Shustanov and P. Yakimov, “CNN Design for Real-Time Traffic Sign Recognition,” Procedia Eng., vol. 201, pp. 718–725, 2017.
  • [9] Y. LeCun et al., “Comparison of learning algorithms for handwritten digit recognition,” in International conference on artificial neural networks, 1995, vol. 60, pp. 53–60.
  • [10] Philipp Seeböck, “Deep Learning in Medical Image Analysis,” vol. 2015, no. March, pp. 221–248, 2015.
  • [11] U. Kaya, A. Yılmaz, and Y. Dikmen, “Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri,” Eur. J. Sci. Technol., no. 16, pp. 792–808, 2019.
  • [12] V. B. Kumar, S. S. Kumar, and V. Saboo, “Dermatological Disease Detection Using Image Processing and Machine Learning,” 2016 3rd Int. Conf. Artif. Intell. Pattern Recognition, AIPR 2016, pp. 88–93, 2016.
  • [13] S. Jain, V. Jagtap, and N. Pise, “Computer aided melanoma skin cancer detection using image processing,” Procedia Comput. Sci., vol. 48, no. C, pp. 735–740, 2015.
  • [14] A. Chaudhary and S. S. Singh, “Lung cancer detection on CT images by using image processing,” Proc. Turing 100 - Int. Conf. Comput. Sci. ICCS 2012, pp. 142–146, 2012.
  • [15] P. Kumar Mallick, S. H. Ryu, S. K. Satapathy, S. Mishra, G. N. Nguyen, and P. Tiwari, “Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network,” IEEE Access, vol. 7, pp. 46278–46287, 2019.
  • [16] M. J. Horry et al., “COVID-19 Detection through Transfer Learning Using Multimodal Imaging Data,” IEEE Access, vol. 8, pp. 149808–149824, 2020.
  • [17] M. Toğaçar, B. Ergen, and Z. Cömert, “Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks,” Med. Biol. Eng. Comput., vol. 59, no. 1, pp. 57–70, 2021.
  • [18] A. A. Abbasi et al., “Detecting prostate cancer using deep learning convolution neural network with transfer learning approach,” Cogn. Neurodyn., vol. 14, no. 4, pp. 523–533, 2020.
  • [19] T. Rahman et al., “Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray,” Appl. Sci., vol. 10, no. 9, 2020.
  • [20] F. J. Cazorla et al., “PROXIMA: Improving Measurement-Based Timing Analysis through Randomisation and Probabilistic Analysis,” Proc. - 19th Euromicro Conf. Digit. Syst. Des. DSD 2016, pp. 276–285, 2016.
  • [21] R. I. Davis and L. Cucu-Grosjean, “A Survey of Probabilistic Schedulability Analysis Techniques for Real-Time Systems,” Leibniz Trans. Embed. Syst., vol. 6, no. 1, pp. 04:1–04:53, 2019.
  • [22] J. Abella, D. Hardy, I. Puaut, E. Quinones, and F. J. Cazorla, “On the comparison of deterministic and probabilistic WCET estimation techniques,” Proc. - Euromicro Conf. Real-Time Syst., pp. 266–275, 2014.
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  • [25] B. Lesage, D. Griffin, S. Altmeyer, L. Cucu-Grosjean, and R. I. Davis, “On the analysis of random replacement caches using static probabilistic timing methods for multi-path programs,” Real-Time Syst., vol. 54, no. 2, pp. 307–388, 2018.
  • [26] Vijayalakshmi A and Rajesh Kanna B, “Deep learning approach to detect malaria from microscopic images,” Multimed. Tools Appl., vol. 79, no. 21–22, pp. 15297–15317, 2020.
  • [27] Y. Dong et al., “Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells,” 2017 IEEE EMBS Int. Conf. Biomed. Heal. Informatics, BHI 2017, pp. 101–104, 2017.
  • [28] F. Yang et al., “Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 5, pp. 1427–1438, 2020.
  • [29] W. D. Pan, Y. Dong, and D. Wu, “Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks,” Mach. Learn. - Adv. Tech. Emerg. Appl., 2018.
  • [30] A. Sai Bharadwaj Reddy and D. Sujitha Juliet, “Transfer learning with RESNET-50 for malaria cell-image classification,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 945–949, 2019.
  • [31] K. M. F. Fuhad, J. F. Tuba, M. R. A. Sarker, S. Momen, N. Mohammed, and T. Rahman, “Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application,” Diagnostics, vol. 10, no. 5, 2020.
  • [32] S. Rajaraman et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, vol. 6, p. e4568, 2018.
  • [33] “Malaria Cell Images Dataset.” [Online]. Available: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria.
  • [34] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2018.
  • [35] B. Bayar and M. C. Stamm, “A deep learning approach to universal image manipulation detection using a new convolutional layer,” IH MMSec 2016 - Proc. 2016 ACM Inf. Hiding Multimed. Secur. Work., pp. 5–10, 2016.
  • [36] D. Miao, W. Pedrycz, D. Ślezak, G. Peters, Q. Hu, and R. Wang, “Mixed Pooling for Convolutional Neural Networks,” in International Conference on Rough Sets and Knowledge Technology, 2014, vol. 8818, pp. 364–375.
  • [37] M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, “Learning Pooling for Convolutional Neural Network,” Neurocomputing, vol. 224, no. April 2016, pp. 96–104, 2017.
  • [38] S. Postalcıloǧlu, “Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition,” Int. J. Pattern Recognit. Artif. Intell., vol. 34, no. 2, 2020.
  • [39] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “2012 AlexNet,” Adv. Neural Inf. Process. Syst. pp. 1–9, 2012.

Yıl 2022, Cilt: 5 Sayı: 3, 427 - 447, 31.12.2022

Emel Soylu

https://doi.org/10.35377/saucis...1197119
Cited By: 1

Öz

Kaynakça

  • [1] “Sıtma.” [Online]. Available: https://hsgm.saglik.gov.tr/tr/zoonotikvektorel-sitma/detay.html.
  • [2] WHO, World malaria report 2020- WHO. 2020.
  • [3] “What is malaria?,” Global Health, Division of Parasitic Diseases and Malaria, 2021. [Online]. Available: https://www.cdc.gov/.
  • [4] E. Soylu, T. Soylu, and R. Bayir, “Design and implementation of SOC prediction for a Li-Ion battery pack in an electric car with an embedded system,” Entropy, vol. 19, no. 4, 2017.
  • [5] Y. Karabacak and A. Uysal, “Fuzzy logic controlled brushless direct current motor drive design and application for regenerative braking,” in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017, pp. 1–7.
  • [6] A. Uysal, S. Gokay, E. Soylu, T. Soylu, and S. Çaşka, “Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication,” Meas. Control (United Kingdom), vol. 52, no. 7–8, 2019.
  • [7] L. V. Selby, W. R. Narain, A. Russo, V. E. Strong, and P. Stetson, “Autonomous detection, grading, and reporting of postoperative complications using natural language processing,” Surg. (United States), vol. 164, no. 6, pp. 1300–1305, 2018.
  • [8] A. Shustanov and P. Yakimov, “CNN Design for Real-Time Traffic Sign Recognition,” Procedia Eng., vol. 201, pp. 718–725, 2017.
  • [9] Y. LeCun et al., “Comparison of learning algorithms for handwritten digit recognition,” in International conference on artificial neural networks, 1995, vol. 60, pp. 53–60.
  • [10] Philipp Seeböck, “Deep Learning in Medical Image Analysis,” vol. 2015, no. March, pp. 221–248, 2015.
  • [11] U. Kaya, A. Yılmaz, and Y. Dikmen, “Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri,” Eur. J. Sci. Technol., no. 16, pp. 792–808, 2019.
  • [12] V. B. Kumar, S. S. Kumar, and V. Saboo, “Dermatological Disease Detection Using Image Processing and Machine Learning,” 2016 3rd Int. Conf. Artif. Intell. Pattern Recognition, AIPR 2016, pp. 88–93, 2016.
  • [13] S. Jain, V. Jagtap, and N. Pise, “Computer aided melanoma skin cancer detection using image processing,” Procedia Comput. Sci., vol. 48, no. C, pp. 735–740, 2015.
  • [14] A. Chaudhary and S. S. Singh, “Lung cancer detection on CT images by using image processing,” Proc. Turing 100 - Int. Conf. Comput. Sci. ICCS 2012, pp. 142–146, 2012.
  • [15] P. Kumar Mallick, S. H. Ryu, S. K. Satapathy, S. Mishra, G. N. Nguyen, and P. Tiwari, “Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network,” IEEE Access, vol. 7, pp. 46278–46287, 2019.
  • [16] M. J. Horry et al., “COVID-19 Detection through Transfer Learning Using Multimodal Imaging Data,” IEEE Access, vol. 8, pp. 149808–149824, 2020.
  • [17] M. Toğaçar, B. Ergen, and Z. Cömert, “Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks,” Med. Biol. Eng. Comput., vol. 59, no. 1, pp. 57–70, 2021.
  • [18] A. A. Abbasi et al., “Detecting prostate cancer using deep learning convolution neural network with transfer learning approach,” Cogn. Neurodyn., vol. 14, no. 4, pp. 523–533, 2020.
  • [19] T. Rahman et al., “Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray,” Appl. Sci., vol. 10, no. 9, 2020.
  • [20] F. J. Cazorla et al., “PROXIMA: Improving Measurement-Based Timing Analysis through Randomisation and Probabilistic Analysis,” Proc. - 19th Euromicro Conf. Digit. Syst. Des. DSD 2016, pp. 276–285, 2016.
  • [21] R. I. Davis and L. Cucu-Grosjean, “A Survey of Probabilistic Schedulability Analysis Techniques for Real-Time Systems,” Leibniz Trans. Embed. Syst., vol. 6, no. 1, pp. 04:1–04:53, 2019.
  • [22] J. Abella, D. Hardy, I. Puaut, E. Quinones, and F. J. Cazorla, “On the comparison of deterministic and probabilistic WCET estimation techniques,” Proc. - Euromicro Conf. Real-Time Syst., pp. 266–275, 2014.
  • [23] C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
  • [24] gem5, “gem5 homepage,” 2018. .
  • [25] B. Lesage, D. Griffin, S. Altmeyer, L. Cucu-Grosjean, and R. I. Davis, “On the analysis of random replacement caches using static probabilistic timing methods for multi-path programs,” Real-Time Syst., vol. 54, no. 2, pp. 307–388, 2018.
  • [26] Vijayalakshmi A and Rajesh Kanna B, “Deep learning approach to detect malaria from microscopic images,” Multimed. Tools Appl., vol. 79, no. 21–22, pp. 15297–15317, 2020.
  • [27] Y. Dong et al., “Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells,” 2017 IEEE EMBS Int. Conf. Biomed. Heal. Informatics, BHI 2017, pp. 101–104, 2017.
  • [28] F. Yang et al., “Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 5, pp. 1427–1438, 2020.
  • [29] W. D. Pan, Y. Dong, and D. Wu, “Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks,” Mach. Learn. - Adv. Tech. Emerg. Appl., 2018.
  • [30] A. Sai Bharadwaj Reddy and D. Sujitha Juliet, “Transfer learning with RESNET-50 for malaria cell-image classification,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 945–949, 2019.
  • [31] K. M. F. Fuhad, J. F. Tuba, M. R. A. Sarker, S. Momen, N. Mohammed, and T. Rahman, “Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application,” Diagnostics, vol. 10, no. 5, 2020.
  • [32] S. Rajaraman et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, vol. 6, p. e4568, 2018.
  • [33] “Malaria Cell Images Dataset.” [Online]. Available: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria.
  • [34] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2018.
  • [35] B. Bayar and M. C. Stamm, “A deep learning approach to universal image manipulation detection using a new convolutional layer,” IH MMSec 2016 - Proc. 2016 ACM Inf. Hiding Multimed. Secur. Work., pp. 5–10, 2016.
  • [36] D. Miao, W. Pedrycz, D. Ślezak, G. Peters, Q. Hu, and R. Wang, “Mixed Pooling for Convolutional Neural Networks,” in International Conference on Rough Sets and Knowledge Technology, 2014, vol. 8818, pp. 364–375.
  • [37] M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, “Learning Pooling for Convolutional Neural Network,” Neurocomputing, vol. 224, no. April 2016, pp. 96–104, 2017.
  • [38] S. Postalcıloǧlu, “Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition,” Int. J. Pattern Recognit. Artif. Intell., vol. 34, no. 2, 2020.
  • [39] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “2012 AlexNet,” Adv. Neural Inf. Process. Syst. pp. 1–9, 2012.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Emel Soylu 0000-0003-2774-9778

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 31 Ekim 2022
Kabul Tarihi 30 Kasım 2022
Yayımlandığı Sayı Yıl 2022Cilt: 5 Sayı: 3

Kaynak Göster

IEEE E. Soylu, “A Deep Transfer Learning-Based Comparative Study for Detection of Malaria Disease”, SAUCIS, c. 5, sy. 3, ss. 427–447, 2022, doi: 10.35377/saucis...1197119.

Cited By

Classification of Electronics Components using Deep Learning
Sakarya University Journal of Computer and Information Sciences
https://doi.org/10.35377/saucis...1391636

29070  The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License