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Volume 15, Issue 3 ( Special Issue (AI in Medicine) - August 2023 2023)
Iranian Journal of Blood and Cancer 2023, 15(3): 112-124 |
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Fasihfar Z, Rokhsati H, Sadeghsalehi H, Ghaderzadeh M, Gheisari M. AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites. Iranian Journal of Blood and Cancer 2023; 15 (3) :112-124
URL: http://ijbc.ir/article-1-1416-en.html
URL: http://ijbc.ir/article-1-1416-en.html
Zohre Fasihfar1 
, Hamidreza Rokhsati2 , Hamidreza Sadeghsalehi3 , Mustafa Ghaderzadeh * 4, Mehdi Gheisari5
, Hamidreza Rokhsati2 , Hamidreza Sadeghsalehi3 , Mustafa Ghaderzadeh * 4, Mehdi Gheisari5
1- Faculty Member, Electrical and Computer Engineering Department, Hakim Sabzevari University, Iran
2- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
3- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
4- Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran , Mustaf.ghaderzadeh@sbmu.ac.ir
5- Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, India
2- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
3- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
4- Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran , Mustaf.ghaderzadeh@sbmu.ac.ir
5- Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences, Chennai, India
Abstract: (376 Views)
Background: Malaria remains a significant global health problem, with a high incidence of cases and a substantial number of deaths yearly. Early identification and accurate diagnosis play a crucial role in effective malaria treatment. However, underdiagnosis presents a significant challenge in reducing mortality rates, and traditional laboratory diagnosis methods have limitations in terms of time consumption and error susceptibility. To overcome these challenges, researchers have increasingly utilized Machine Learning techniques, specifically neural networks, which provide faster, cost-effective, and highly accurate diagnostic capabilities.
Methods: This study aimed to compare the performance of a traditional neural network (NN) with a convolutional neural network (CNN) in the diagnosis and classification of different types of malaria using blood smear images. We curated a comprehensive malaria dataset comprising 1,920 images obtained from 84 patients suspected of having various malaria strains. The dataset consisted of 624 images of Falciparum, 548 images of Vivax, 588 images of Ovale, and 160 images from suspected healthy individuals, obtained from local hospitals in Iran. To ensure precise analysis, we developed a unique segmentation model that effectively eliminated therapeutically beneficial cells from the image context, enabling accurate analysis using artificial intelligence algorithms.
Results: The evaluation of the traditional NN and the proposed 6-layer CNN model for image classification yielded average accuracies of 95.11% and 99.59%, respectively. These results demonstrate that the CNN, as a primary algorithm of deep neural networks (DNN), outperforms the traditional NN in analyzing different classes of malaria images. The CNN model demonstrated superior diagnostic performance, delivering enhanced accuracy and reliability in the classifying of malaria cases.
Conclusion: This research underscores the potential of ML technologies, specifically CNNs, in improving malaria diagnosis and classification. By leveraging advanced image analysis techniques, including the developed segmentation model, CNN showcased remarkable proficiency in accurately identifying and classifying various malaria parasites from blood smear images. The adoption of machine learning-based approaches holds promise for more effective management and treatment of malaria, addressing the challenges of underdiagnosis and improving patient outcomes.
Methods: This study aimed to compare the performance of a traditional neural network (NN) with a convolutional neural network (CNN) in the diagnosis and classification of different types of malaria using blood smear images. We curated a comprehensive malaria dataset comprising 1,920 images obtained from 84 patients suspected of having various malaria strains. The dataset consisted of 624 images of Falciparum, 548 images of Vivax, 588 images of Ovale, and 160 images from suspected healthy individuals, obtained from local hospitals in Iran. To ensure precise analysis, we developed a unique segmentation model that effectively eliminated therapeutically beneficial cells from the image context, enabling accurate analysis using artificial intelligence algorithms.
Results: The evaluation of the traditional NN and the proposed 6-layer CNN model for image classification yielded average accuracies of 95.11% and 99.59%, respectively. These results demonstrate that the CNN, as a primary algorithm of deep neural networks (DNN), outperforms the traditional NN in analyzing different classes of malaria images. The CNN model demonstrated superior diagnostic performance, delivering enhanced accuracy and reliability in the classifying of malaria cases.
Conclusion: This research underscores the potential of ML technologies, specifically CNNs, in improving malaria diagnosis and classification. By leveraging advanced image analysis techniques, including the developed segmentation model, CNN showcased remarkable proficiency in accurately identifying and classifying various malaria parasites from blood smear images. The adoption of machine learning-based approaches holds promise for more effective management and treatment of malaria, addressing the challenges of underdiagnosis and improving patient outcomes.
Keywords: Malaria Parasites, Image Processing, Artificial Neural Network, Deep Learning, Convolutional Neural Network
: Original Article |
Subject:
AI in Medicine
Received: 2023/06/9 | Accepted: 2023/08/18 | Published: 2023/09/17
Received: 2023/06/9 | Accepted: 2023/08/18 | Published: 2023/09/17
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