Comparison of EfficientNetB0 and EfficientNetB7 Models in Classifying Malaria Based on Blood Cells

Authors

  • Muhammad Rizkiansyah Maulana Kiki Department of Informatics, University of Muhammadiyah Malang, Malang, Indonesia
  • Zamah Sari Department of Informatics, University of Muhammadiyah Malang, Malang, Indonesia
  • Didih Rizki Department of Informatics, University of Muhammadiyah Malang, Malang, Indonesia

DOI:

https://doi.org/10.15575/join.v9i2.1195

Keywords:

Classification, Convolutional Neural Network, EfficientNet , Malaria

Abstract

Malaria is a disease caused by the bite of malaria mosquitoes, which spreads through blood. Malaria mosquitoes will spread the Plasmodium parasite through their bites. Early malaria identification is essential so the disease can be prevented immediately. Through data science, which utilizes the CNN model, the classification of blood infected with parasites can be predicted accurately. This research uses data obtained from Kaggle website with 27,558 image samples. The data is divided into two classes, parasite-infected and uninfected, which are then divided again into two types. The first class is training data divided into 80% of the total data and the other 20% as validation data. This research used two test scenarios to obtain a more effective classification model. The first scenario uses Hyperparameter Tuning and the EfficientNetB0 model with classification results of 95%. Meanwhile, the classification achievement for scenario two was 99% by utilizing EfficientNetB7.

References

[1] N. K. C. PRATIWI, N. IBRAHIM, Y. N. FU’ADAH, and S. RIZAL, “Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 9, no. 2, p. 306, 2021, doi: 10.26760/elkomika.v9i2.306.

[2] R. Puasa, A. A. H, and A. Kader, “Identifikasi Plasmodium Malaria Didesa Beringin Jaya Kecamatan Oba Tengah Kota Tidore Kepulauan,” J. Ris. Kesehat., vol. 7, no. 1, p. 21, 2018, doi: 10.31983/jrk.v7i1.3056.

[3] M. R. Islam et al., “Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images,” Sensors, vol. 22, no. 12, pp. 1–20, 2022, doi: 10.3390/s22124358.

[4] P. Chanda-Kapata, N. Kapata, and A. Zumla, “COVID-19 and malaria: A symptom screening challenge for malaria endemic countries,” Int. J. Infect. Dis., vol. 94, pp. 151–153, 2020, doi: 10.1016/j.ijid.2020.04.007.

[5] Y. Yohannes, S. Devella, and K. Arianto, “Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency,” JUITA J. Inform., vol. 8, no. 1, p. 37, 2020, doi: 10.30595/juita.v8i1.6671.

[6] WHO, W. H. O. (2020) World Malaria Report 2020. Diambil 1 april 2023, dari https://www.who.int/publications/i/item/9789240015791

[7] J. E. Mosso and C. Song, “Distribusi prevalensi infeksi Plasmodium serta gambaran kepadatan parasit dan jumlah limfosit absolut pada penderita malaria di RSUD Kabupaten Manokwari periode Januari – Maret 2019,” Tarumanagara Med. J., vol. 2, no. 2, pp. 320–330, 2020, doi: 10.24912/tmj.v3i1.9735.

[8] A. Alim, A. Adam, and B. Dimi, “Prevalensi Malaria Berdasarkan Karakteristik Sosio Demografi,” J. Ilm. Kesehat., vol. 19, no. 01, pp. 4–9, 2020, doi: 10.33221/jikes.v19i01.399.

[9] J. Talapko, I. Škrlec, T. Alebić, M. Jukić, and A. Včev, “Malaria: The past and the present,” Microorganisms, vol. 7, no. 6, 2019, doi: 10.3390/microorganisms7060179.

[10] Y. Gustiani, K. Pramudho, and D. Sadik, “Analisis Faktor Yang Berhubungan Dengan 38 Pencegahan Penyakit Malaria,” J. Ilm. Permas J. Ilm. STIKES Kendal, vol. 11, pp. 1– 22, 2021, [Online]. Available: http://journal.stikeskendal.ac.id/index.php/PSKM.

[11] M. Harahap, J. Jefferson, S. Barti, S. Samosir, and C. A. Turnip, “Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria,” SinkrOn, vol. 5, no. 2, pp. 199–207, 2020, doi: 10.33395/sinkron.v5i2.10713.

[12] A. W. Setiawan et al., “Deteksi Malaria Berbasis Segmentasi Warna Citra Dan Malaria Detection Using Color Image Segmentation and Machine,” vol. 8, no. 4, pp. 769–776, 2021, doi: 10.25126/jtiik.202184377.

[13] L. A. Andika, H. Pratiwi, and S. S. Handajani, “Klasifikasi Penyakit Pneumonia Menggunakan Metode Convolutional Neural Network Dengan Optimasi Adaptive Momentum,” Indones. J. Stat. Its Appl., vol. 3, no. 3, pp. 331–340, 2019, doi: 10.29244/ijsa.v3i3.560.

[14] M. Z. Ersyad, K. N. Ramadhani, A. Arifianto, and L. Belakang, “Pengenalan Bentuk Tangan dengan Convolutional Neural Network (CNN),” e-Proceeding Eng., vol. 7, no. 2, pp. 8212–8222, 2020.

[15] V. M. P. Salawazo, D. P. J. Gea, R. F. Gea, and F. Azmi, “Implementasi Metode Convolutional Neural Network ( CNN ) Pada Peneganalan Objek Video Cctv,” J. Mantik Penusa, vol. 3, no. 1, pp. 74–79, 2019.

[16] C. Garbin, X. Zhu, and O. Marques, “Dropout vs. batch normalization: an empirical study of their impact to deep learning,” Multimed. Tools Appl., vol. 79, no. 19–20, pp. 12777–12815, 2020, doi: 10.1007/s11042-019-08453-9.

[17] K. Sriporn, C.-F. Tsai, C.-E. Tsai, and P. Wang, “Analyzing Malaria Disease Using Effective Deep Learning Approach,” Diagnostics, pp. 1–22. doi.org/10.3390/diagnostics10100744

[18] I. G. S. M. Diyasa, A. Fauzi, A. Setiawan, M. Idhom, R. R. Wahid, and A. D. Alhajir, “Pre-Trained Deep Convolutional Neural Network for Detecting Malaria on the Human Blood Smear Images,” 3rd Int. Conf. Artif. Intell. Inf. Commun. ICAIIC 2021, pp. 235– 240, 2021, doi: 10.1109/ICAIIC51459.2021.9415183.

[19] Mbanefo A, Kumar N. Evaluation of Malaria Diagnostic Methods as a Key for Successful Control and Elimination Programs. Trop Med Infect Dis. 2020 Jun 19;5(2):102. doi: 10.3390/tropicalmed5020102.

[20] I. G. S. M. Diyasa, A. Fauzi, A. Setiawan, M. Idhom, R. R. Wahid, and A. D. Alhajir, “Pre-Trained Deep Convolutional Neural Network for Detecting Malaria on the Human Blood Smear Images,” 3rd Int. Conf. Artif. Intell. Inf. Commun. ICAIIC 2021, pp. 235– 240, 2021, doi: 10.1109/ICAIIC51459.2021.9415183.

[21] M. M. QANBAR and S. TASDEMIR, “Detection of Malaria Diseases with Residual Attention Network,” IJISAE, vol. 7, no. 4, pp. 238–244, 2019, doi: https://doi.org/10.18201/ijisae.2019457677

[22] D. Shah, K. Kawale, M. Shah, S. Randive, and R. Mapari, “Malaria Parasite Detection Using Deep Learning: (Beneficial to humankind),” Proc. Int. Conf. Intell. Comput. Control Syst. ICICCS 2020, no. Iciccs, pp. 984–988, 2020, doi: 10.1109/ICICCS48265.2020.9121073.

[23] kaggle datasets download -d iarunava/cell-images-for-detecting-malaria

[24] X. Xiao, M. Yan, S. Basodi, C. Ji, and Y. Pan, "Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm," arXiv, 2020.

https://doi.org/10.48550/arXiv.2006.12703

[25] M. Aji. Purnama Wibowo, Muhammad Bima Al Fayyadl, Yufis Azhar, and Zamah Sari, “Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 4, pp. 538–547, 2022, doi: 10.29207/resti.v6i4.4119.

[26] D. R. Nayak, N. Padhy, P. K. Mallick, M. Zymbler, and S. Kumar, “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, 2022, doi: 10.3390/axioms11010034.

[27] Ulfah Nur Oktaviana and Yufis Azhar, “Klasifikasi Sampah Menggunakan Ensemble DenseNet169,” Resti, vol. 1, no. 1, pp. 19–25, 2021, doi: https://doi.org/10.29207/resti.v5i6.3673

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2024-12-27

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