Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Dong, Yuhang; Pan, W. David; Wu, Dongsheng

doi:10.3390/e21111062

Cited by 13 publications

(7 citation statements)

References 46 publications

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“…Deep learning techniques can effectively accomplish the tasks of image detection, recognition and classi cation, so the introduction of deep learning techniques in the eld of imaging may help radiologists to complete various tasks of detection and diagnosis [19,20]. Pulmonary nodule detection using AI algorithm is an important part of AI medical eld [21].…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Intelligence in Human Diagnosis and Public Health Management

Wang

Zhao

Jia

2021

Preprint

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To improve the diagnostic efficiency and accuracy of corona virus disease 2019 (COVID-19), and to study the application of artificial intelligence (AI) in COVID-19 diagnosis and public health management, the computer tomography (CT) image data of 200 COVID-19 patients are collected, and the image is input into the AI auxiliary diagnosis software based on the deep learning model, "uAI the COVID-19 intelligent auxiliary analysis system", for focus detection. The software automatically carries on the pneumonia focus identification and the mark in batches, and automatically calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes, and in density, the most common shadow is the ground glass opacity. The detection rate of manual detection method is 95.30%, misdiagnosis rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of AI software focus detection method based on deep learning model is 99.76%, the misdiagnosis rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, it can effectively identify COVID-19 focus and provide relevant data information of focus to provide objective data support for COVID-19 diagnosis and public health management.

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Section: Discussionmentioning

confidence: 99%

Application of Artificial Intelligence in Human Diagnosis and Public Health Management

Wang

Zhao

Jia

2021

Preprint

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“…The entire image contained around 1,000,000 red blood cells, with at least 0.2% samples infected by the malaria parasites. We performed several image morphological operations to crop each cell out [ 32 ]. Then, we used the support vector machine (SVM) to classify cells based on several selected features [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Dong

Pan

2022

J. Imaging

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Digital images are usually stored in compressed format. However, image classification typically takes decompressed images as inputs rather than compressed images. Therefore, performing image classification directly in the compression domain will eliminate the need for decompression, thus increasing efficiency and decreasing costs. However, there has been very sparse work on image classification in the compression domain. In this paper, we studied the feasibility of classifying images in their JPEG compression domain. We analyzed the underlying mechanisms of JPEG as an example and conducted classification on data from different stages during the compression. The images we used were malaria-infected red blood cells and normal cells. The training data include multiple combinations of DCT coefficients, DC values in both decimal and binary forms, the “scan” segment in both binary and decimal form, and the variable length of the entire bitstream. The result shows that LSTM can successfully classify the image in its compressed form, with accuracies around 80%. If using only coded DC values, we can achieve accuracies higher than 90%. This indicates that images from different classes can still be well separated in their JPEG compressed format. Our simulations demonstrate that the proposed compression domain-processing method can reduce the input data, and eliminate the image decompression step, thereby achieving significant savings on memory and computation time.

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“…This disease is caused by the plasmodium from the Anopheles mosquito which is transmitted through the bite of the mosquito. Plasmodium carried from the bite of female Anopheles mosquitoes will live and multiply in human red blood cells which, if not handled quickly and appropriately, can cause death (Widiawati et al, 2016) (Liang et al, 2017) (Quan et al, 2020) (Dong et al, 2019). Based on data from the World Health Organization (WHO), malaria cases worldwide until 2018 reached 228 million with a total death rate of 405,000 per year which includes the Southeast Asian region, especially Indonesia (World Health Organization, 2019b).…”

Section: Introduction *mentioning

confidence: 99%

Image Recognition of Malaria-infected Red Blood Cells among Other Normal and Cancer-Mutated Cells Using CNN

Nivaan

2022

jinav j. inf. vis.

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Malaria is a contagious infectious disease that is still threatening human life. Malaria morbidity when viewed by province shows that Eastern Indonesia is the area with the highest Annual Parasite Incidence (API), namely Papua, West Papua, NTT, and Maluku. This is a concern for the continued efforts to control and eliminate malaria in these high malaria-endemic areas. There are many strategies to help and prevent, include the possibility of innovation in the diagnostic process. Therefore, to answer how to provide innovation in technology to accelerate the elimination of malaria, this study aims to identify the image of red blood cells which infected with malaria among other normal and leukemia cancer-mutated cells (non-malaria) by making improvements through the proposed new model used. This model is meant to do deep learning using Convolutional Neural Network (CNN). The results obtained in this study show that the success of using the proposed model is influenced by the pre-processing stage, the dropout regularization function, learning rate, and momentum value used. The accuracy value obtained is 0.9660, 0.9693 precision, 0.9626 recall, and an F1 score of 0.9659.

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Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Cited by 13 publications

References 46 publications

Application of Artificial Intelligence in Human Diagnosis and Public Health Management

Application of Artificial Intelligence in Human Diagnosis and Public Health Management

Image Classification in JPEG Compression Domain for Malaria Infection Detection

Image Recognition of Malaria-infected Red Blood Cells among Other Normal and Cancer-Mutated Cells Using CNN

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