Araştırma Makalesi
Yıl 2024,
Cilt: 39 Sayı: 1, 197 - 210, 28.03.2024
Öz
This article highlights that malaria is widespread worldwide through infected mosquitoes transmitted to humans and is caused by the blood parasite Plasmodium. Early diagnosis and treatment of malaria plays an important role in reducing morbidity and mortality, especially in developing countries. The traditional method of diagnosing malaria involves examining red blood cells under a microscope, but this method can be inefficient as it relies on expert knowledge. A highlight of the paper is the use of machine learning methods in malaria diagnosis. In particular, a Convolutional Neural Network (CNN) architecture is proposed to detect parasitized and non-parasitized cells. Furthermore, the performance of this proposed model is compared with pre-trained CNN architectures such as VGG-19, InceptionResNetV2, DenseNet121 and EfficientNetB3. In the experiments, the Malaria Dataset published by the National Institute of Health (NIH) was used and the proposed CNN architecture achieved 98.9% accuracy. These results show that the proposed model is effective in accurately recognizing cell images containing Plasmodium. This study highlights the potential of AI-based methods in the field of malaria diagnosis.
Anahtar Kelimeler
Kaynakça
- 1. Mahajan, H., Rashid, A., Junnarkar, A., 2022. Integration of Healthcare 4.0 and Blockchain Into Secure Cloud-Based Electronic Health Records Systems. Appl Nanoscience, 13, 2329-2342
- 2. Mbanefo, A., Kumar, N., 2020. Evaluation of Malaria Diagnostic Methods as a Key for Successful Control and Elimination Programs. Trop Med Infect Disease, 5(2), 102
- 3. Nema, S., Rahi, M., Sharma, A., Bharti, P.K., 2022. Strengthening Malaria Microscopy Using Artificial Intelligence-Based Approaches in India. Lancet Reg Health-Southeast Asia, 3(5), 100054
- 4. W.H. Organization, 2021. Malaria Microscopy Quality Assurance Manual-Version 2. World Health Organization, 140.
- 5. Alhayani, A., Bilal, S.A., Hamid, N., 2022. Optimized Video Internet of Things Using Elliptic Curve Cryptography Based Encryption And Decryption. Comput Electr Eng., 101, 108022.
- 6. Alhayani, B., Kwekha-Rashid, A.S., Mahajan, H.B., 2022. Standards For The Industry 4.0 Enabled Communication Systems Using Artificial Intelligence: Perspective of Smart Healthcare System. Appl Nanoscience, 13, 1807-1817.
- 7. Daid, R., Kumar, Y., Gupta, A., Kaur, I., 2021. An Effective Mechanism for Early Chronic Illness Detection Using Multilayer Convolution Deep Learning Predictive Modelling. In 2021 International Conference on Technological Advancements and Innovations (ICTAI), IEEE, 649-652.
- 8. Bansal, K., Bathla, R.K., Kumar, Y., 2022. Deep Transfer Learning Techniques with Hybrid Optimization iIn Early Prediction and Diagnosis of Different Types of Oral Cancer. Soft Comput 26(21), 11153-11184.
- 9. Beck, H.P., 2022. Digital Microscopy and Artificial Intelligence Could Profoundly Contribute to Malaria Diagnosis in Elimination Settings. Front Artif Intell, 17(5), 510483.
- 10. Kumar, Y., Koul, A., Mahajan, S., 2022. A Deep Learning Approaches and Fastai Text Classification to Predict 25 Medical Diseases from Medical Speech Utterances, Transcription and Intent. Soft Comput, 26(17), 8253-8272.
- 11. Narayanan, B.N., Ali, R., Hardie, R.C., 2019. Performance Analysis of Machine Learning and Deep Learning Architectures for Malaria Detection on Cell Images. In: Zelinski ME, Taha TM, Howe J, Awwal AAS, Iftekharuddin KM (Eds) Applications of Machine Learning. SPIE, Bellingham, 11139, 240-249.
- 12. Alsunbuli, B.N., Ismail, W., Mahyuddin, N.M., 2021. Convolutional Neural Network and Kalman Filter-Based Accurate CSI Prediction for Hybrid Beamforming under A Minimized Blockage Effect in Millimeter-Wave Network. Appl Nanosci, 13, 1539-1560.
- 13. Aslan, E., Özüpak, Y., 2024. Classification of Blood Cells with Convolutional Neural Network Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1), 314-326.
- 14. Malihi, L., Ansari-Asl, K., Behbahani, A., 2013. Malaria Parasite Detection in Giemsa-Stained Blood Cell Images, 8th Iranian Conference on Machine Vision And Image Processing (MVIP), Zanjan, 360-365.
- 15. Memeu A., Daniel, M., 2014. A Rapid Malaria Diagnostic Method Based on Automatic Detection and Classification of Plasmodium Parasites in Stained thin Blood Smear Images. University of Nairobi, 44(1), 69-78.
- 16. Prasad, K., Winter, J., Bhat, U.M., Acharya, R.V., Prabhu, G.K., 2012. Image Analysis Approach for Development of A Decision Support System for Detection of Malaria Parasites ,n Thin Blood Smear Images. J Digit Imaging, 25(4), 542-549.
- 17. Kumarasamy, S.K., Ong, S.H., Tan, K., 2011. Robust Contour Reconstruction of Red Blood Cells and Parasites in the Automated Identification of the Stages of Malarial Infection. Mach Vis Appl., 22(3), 461-469.
- 18. Liang, Z., Fulfilmenti, P., 2017. Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach. The Degree Master of Arts.
- 19. Quan, Q., Wang, J., Liu, L., 2020. An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases. Interdiscip Sci Comput Life Sci., 12, 217-225.
- 20. Rajaraman, S., Antani, S., Pootschi, M., Silamut, K., Hossain, M., 2018. Pre-Trained Convolutional Networks as Feature Extractors Toward Improved Malaria Parasite Detection in Thin Blood Smear Images. Peer J., 6(4), 4578.
- 21. Kakkar, B., Goyal, M., Johri, P., 2023. Artificial Intelligence-Based Approaches for Detection and Classification of Different Classes of Malaria Parasites Using Microscopic Images: A Systematic Review. Arch Computat Methods Eng, 30, 4781-4800.
- 22. Neha, S., Radim, B., Malay, D., 2022. A Vision Transformer Based Approach for Analysis of Plasmodium Vivax Life Cycle for Malaria Prediction Using Thin Blood Smear Microscopic Images. Computer Methods and Programs in Biomedicine, 224, 106996.
- 23. Gourisaria, M.K., Das, S., Sharma, R., Rautaray, S.S., Pandey, M., 2020. A Deep Learning Model for Malaria Disease Detection and Analysis Using Deep Convolutional Neural Networks. International Journal on Emerging Technologies, 11(2), 699-704.
- 24. Krishnadas, P., Chadaga, K., Sampathila, N., Rao, S., Swathi, S.K., Prabhu, S., 2022. Classification Of Malaria Using Object Detection Models. Informatics, 9(4), 76-86.
- 25. Muqdad, A., Abdullahi, A.I., 2022. Malaria Parasite Detection Using Deep Learning Algorithms Based on (Cnns) Technique. Computers and Electrical Engineering, 103, 108316.
- 26. Yufeng, Z., Clifford, Y., Alex, M., 2018. Breast Cancer Screening Using Convolutional Neural Network and Follow-Up Digital Mammography. Proc. SPIE 10669, Computational Imaging III. 1066905.
- 27. Hamid, M., Farhad, S., 2020. An Object Based Framework for Building Change Analysis Using 2D and 3D Information of High Resolution Satellite Images. Advances in Space Research, 66(6), 1386-1404.
- 28. Dudeja, T., Dubey, S.K., Bhatt, A.K., 2023. Ensembled EfficientNetB3 Architecture for Multi-Class Classification of Tumours in MRI Images, 395-414.
Yıl 2024,
Cilt: 39 Sayı: 1, 197 - 210, 28.03.2024
Öz
Sıtma, dünyanın birçok bölgesinde yaygın olarak görülen enfekte sivrisineklerin ısırıkları yoluyla insanlara bulaşan parazitlerin neden olduğu hayatı tehdit eden bir hastalıktır. Plasmodium adlı kan paraziti bu hastalığına sebep olmaktadır. Sıtmanın erken teşhisi ve tedavisi, özellikle hastalığın yaygın olduğu gelişmekte olan ülkelerde, hastalık ve ölüm oranlarının azaltılması açısından çok önemlidir. Sıtma teşhisinde kullanılan klasik yöntem, uzmanlar tarafından kırmızı kan hücrelerinin mikroskop yardımıyla incelenmesiyle tespitidir. Bu yöntem, sadece uzmanın bilgi ve deneyimine dayandığı için verimsizdir. Günümüzde hastalığın yüksek oranda doğru bir şekilde tespiti için makine öğrenmesi yöntemleri kullanılmaktadır. Bu çalışmada, hücreyi parazitli veya parazitsiz olarak tespit için Evrişimli Sinir Ağı (ESA) mimarisi önerilmiştir. Önerilen ESA mimarisine ek olarak VGG-19, InceptionResNetV2, DenseNet121 ve EfficientNetB3 gibi önceden eğitilmiş ESA mimarilerinin performansları ile önerdiğimiz modelin performansı karşılaştırılmıştır. Önerdiğimiz ESA mimarisinde National Institute of Health (NIH) tarafından yayınlanan Sıtma Veri Kümesi kullanılarak deneyler gerçekleştirilmiştir. Mimarimiz %98,9 doğruluk ile çalışmaktadır. Çalışmanın sonuçları, Plasmodium içeren hücre görüntülerinin doğruluğunu artırmada etkili olduğunu göstermektedir.
Anahtar Kelimeler
Kaynakça
- 1. Mahajan, H., Rashid, A., Junnarkar, A., 2022. Integration of Healthcare 4.0 and Blockchain Into Secure Cloud-Based Electronic Health Records Systems. Appl Nanoscience, 13, 2329-2342
- 2. Mbanefo, A., Kumar, N., 2020. Evaluation of Malaria Diagnostic Methods as a Key for Successful Control and Elimination Programs. Trop Med Infect Disease, 5(2), 102
- 3. Nema, S., Rahi, M., Sharma, A., Bharti, P.K., 2022. Strengthening Malaria Microscopy Using Artificial Intelligence-Based Approaches in India. Lancet Reg Health-Southeast Asia, 3(5), 100054
- 4. W.H. Organization, 2021. Malaria Microscopy Quality Assurance Manual-Version 2. World Health Organization, 140.
- 5. Alhayani, A., Bilal, S.A., Hamid, N., 2022. Optimized Video Internet of Things Using Elliptic Curve Cryptography Based Encryption And Decryption. Comput Electr Eng., 101, 108022.
- 6. Alhayani, B., Kwekha-Rashid, A.S., Mahajan, H.B., 2022. Standards For The Industry 4.0 Enabled Communication Systems Using Artificial Intelligence: Perspective of Smart Healthcare System. Appl Nanoscience, 13, 1807-1817.
- 7. Daid, R., Kumar, Y., Gupta, A., Kaur, I., 2021. An Effective Mechanism for Early Chronic Illness Detection Using Multilayer Convolution Deep Learning Predictive Modelling. In 2021 International Conference on Technological Advancements and Innovations (ICTAI), IEEE, 649-652.
- 8. Bansal, K., Bathla, R.K., Kumar, Y., 2022. Deep Transfer Learning Techniques with Hybrid Optimization iIn Early Prediction and Diagnosis of Different Types of Oral Cancer. Soft Comput 26(21), 11153-11184.
- 9. Beck, H.P., 2022. Digital Microscopy and Artificial Intelligence Could Profoundly Contribute to Malaria Diagnosis in Elimination Settings. Front Artif Intell, 17(5), 510483.
- 10. Kumar, Y., Koul, A., Mahajan, S., 2022. A Deep Learning Approaches and Fastai Text Classification to Predict 25 Medical Diseases from Medical Speech Utterances, Transcription and Intent. Soft Comput, 26(17), 8253-8272.
- 11. Narayanan, B.N., Ali, R., Hardie, R.C., 2019. Performance Analysis of Machine Learning and Deep Learning Architectures for Malaria Detection on Cell Images. In: Zelinski ME, Taha TM, Howe J, Awwal AAS, Iftekharuddin KM (Eds) Applications of Machine Learning. SPIE, Bellingham, 11139, 240-249.
- 12. Alsunbuli, B.N., Ismail, W., Mahyuddin, N.M., 2021. Convolutional Neural Network and Kalman Filter-Based Accurate CSI Prediction for Hybrid Beamforming under A Minimized Blockage Effect in Millimeter-Wave Network. Appl Nanosci, 13, 1539-1560.
- 13. Aslan, E., Özüpak, Y., 2024. Classification of Blood Cells with Convolutional Neural Network Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1), 314-326.
- 14. Malihi, L., Ansari-Asl, K., Behbahani, A., 2013. Malaria Parasite Detection in Giemsa-Stained Blood Cell Images, 8th Iranian Conference on Machine Vision And Image Processing (MVIP), Zanjan, 360-365.
- 15. Memeu A., Daniel, M., 2014. A Rapid Malaria Diagnostic Method Based on Automatic Detection and Classification of Plasmodium Parasites in Stained thin Blood Smear Images. University of Nairobi, 44(1), 69-78.
- 16. Prasad, K., Winter, J., Bhat, U.M., Acharya, R.V., Prabhu, G.K., 2012. Image Analysis Approach for Development of A Decision Support System for Detection of Malaria Parasites ,n Thin Blood Smear Images. J Digit Imaging, 25(4), 542-549.
- 17. Kumarasamy, S.K., Ong, S.H., Tan, K., 2011. Robust Contour Reconstruction of Red Blood Cells and Parasites in the Automated Identification of the Stages of Malarial Infection. Mach Vis Appl., 22(3), 461-469.
- 18. Liang, Z., Fulfilmenti, P., 2017. Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach. The Degree Master of Arts.
- 19. Quan, Q., Wang, J., Liu, L., 2020. An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases. Interdiscip Sci Comput Life Sci., 12, 217-225.
- 20. Rajaraman, S., Antani, S., Pootschi, M., Silamut, K., Hossain, M., 2018. Pre-Trained Convolutional Networks as Feature Extractors Toward Improved Malaria Parasite Detection in Thin Blood Smear Images. Peer J., 6(4), 4578.
- 21. Kakkar, B., Goyal, M., Johri, P., 2023. Artificial Intelligence-Based Approaches for Detection and Classification of Different Classes of Malaria Parasites Using Microscopic Images: A Systematic Review. Arch Computat Methods Eng, 30, 4781-4800.
- 22. Neha, S., Radim, B., Malay, D., 2022. A Vision Transformer Based Approach for Analysis of Plasmodium Vivax Life Cycle for Malaria Prediction Using Thin Blood Smear Microscopic Images. Computer Methods and Programs in Biomedicine, 224, 106996.
- 23. Gourisaria, M.K., Das, S., Sharma, R., Rautaray, S.S., Pandey, M., 2020. A Deep Learning Model for Malaria Disease Detection and Analysis Using Deep Convolutional Neural Networks. International Journal on Emerging Technologies, 11(2), 699-704.
- 24. Krishnadas, P., Chadaga, K., Sampathila, N., Rao, S., Swathi, S.K., Prabhu, S., 2022. Classification Of Malaria Using Object Detection Models. Informatics, 9(4), 76-86.
- 25. Muqdad, A., Abdullahi, A.I., 2022. Malaria Parasite Detection Using Deep Learning Algorithms Based on (Cnns) Technique. Computers and Electrical Engineering, 103, 108316.
- 26. Yufeng, Z., Clifford, Y., Alex, M., 2018. Breast Cancer Screening Using Convolutional Neural Network and Follow-Up Digital Mammography. Proc. SPIE 10669, Computational Imaging III. 1066905.
- 27. Hamid, M., Farhad, S., 2020. An Object Based Framework for Building Change Analysis Using 2D and 3D Information of High Resolution Satellite Images. Advances in Space Research, 66(6), 1386-1404.
- 28. Dudeja, T., Dubey, S.K., Bhatt, A.K., 2023. Ensembled EfficientNetB3 Architecture for Multi-Class Classification of Tumours in MRI Images, 395-414.
Toplam 28 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Mühendisliği (Diğer), Enerji Sistemleri Mühendisliği (Diğer) |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Mart 2024 |
| Gönderilme Tarihi | 12 Kasım 2023 |
| Kabul Tarihi | 28 Mart 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 39 Sayı: 1 |
