article · Computers, materials & continua/Computers, materials & continua (Print)
This research addresses the challenges of manual malaria diagnosis, which is time-consuming and prone to human error. It proposes a novel Convolutional Neural Network (CNN) model, named Malaria Convolutional Neural Network (MCNN), for the automatic detection and classification of malaria-infected cells from images. The MCNN model aims to improve the efficiency and accuracy of diagnosis, assisting in the computation of parasitemia, which measures infection levels. The model demonstrated high performance across various evaluation metrics, including an accuracy of 0.9924. A comparison with other recent studies showed that the proposed MCNN model outperformed existing works in terms of its diagnostic capabilities.
Accurate and rapid malaria diagnosis is crucial for effective treatment and disease control. This automated system could significantly reduce diagnostic time and human error, leading to quicker interventions and better patient outcomes, particularly in regions with high malaria prevalence and limited resources.
This research presents an applied deep learning model for automated malaria diagnosis, which could be integrated into diagnostic tools for medical professionals and laboratories. The high accuracy and superior performance suggest it is a promising technology for improving diagnostic efficiency and reducing human workload. It appears to be at an advanced stage of research, potentially near-market for clinical application.
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Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN) model is proposed in this work to classify the infected cases. MCNN focuses on detecting infected cells, which aids in the computation of parasitemia, or infection measures. The proposed model achieved 0.9929, 0.9848, 0.9859, 0.9924, 0.0152, 0.0141, 0.0071, 0.9890, 0.9894, and 0.9780 in terms of specificity, sensitivity, precision, accuracy, F1-score, and Matthews Correlation Coefficient, respectively. A comparison was carried out between the proposed model and some recent works in the literature. This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.
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DOI: 10.32604/cmc.2022.025629
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