Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears

Ozsahin, Dilber Uzun; Mustapha, Mubarak Taiwo; Duwa, Basil Bartholomew; Ozsahin, Ilker

doi:10.3390/diagnostics12112702

Cited by 13 publications

(9 citation statements)

References 19 publications

(29 reference statements)

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“…Moreover, the papers in [ 9 , 21 ] demonstrated better performance than ours due to the augmentation techniques. Therefore, the size of the dataset increased, which may help the model generalize better (more data result in a more accurate model [ 32 ]).…”

Section: Results and Evaluationmentioning

confidence: 66%

“…Another approach using in an imbalanced dataset can be found in [ 9 ], where the work was based on building a convolutional neural network. The authors aimed to predict the existence of malaria-infected cells using images obtained by microscopy of thin and thick peripheral blood smears.…”

Section: Related Workmentioning

confidence: 99%

“…Such a manual approach to machine learning for cell image classification of malaria requires experts in machine learning to prepare the model. However, the works [ 7 , 8 ], as well as the greatly different works on machine learning (ML) adopted for the malaria detection problem [ 9 ] suffer from different issues: the first one is that human effort is required in order to extract the most-valuable features to feed the classifiers efficiently. Furthermore, this approach requires specialists in ML and DL to build a robust model, in addition to choosing the best hyperparameters of the model to distinguish the malaria images from the normal ones.…”

Section: Introductionmentioning

confidence: 99%

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Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

et al. 2023

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Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

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Section: Results and Evaluationmentioning

confidence: 66%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

et al. 2023

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“…In order to determine how well a data-driven method performed, it is necessary to compare the predicted values with the actual ones that were collected [ 31 ]. The models were evaluated in this study using several different statistical error measures, as well as the determination coefficient (R2) as a goodness-of-fit measure [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

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The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

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“…The disadvantages of traditional diagnostic methods lead microbiologists to search for new diagnostic methods in the field of microbiology that are faster, cheaper, and more accurate and that guide treatment. Artificial intelligence (AI) implementation in healthcare provides a viable alternative as it has been used in several studies for disease detection, diagnosis, and prediction [10][11][12][13][14]. Implications in AI evolved microbiology to a new diagnostics era, providing many advantages in detecting and identifying microorganisms and leading to optimal treatment strategies [15,16].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model

Çağatan

Mustapha²,

Bagkur³

et al. 2022

Diagnostics

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Cryptococcus neoformans is an opportunistic fungal pathogen with significant medical importance, especially in immunosuppressed patients. It is the causative agent of cryptococcosis. An estimated 220,000 annual cases of cryptococcal meningitis (CM) occur among people with HIV/AIDS globally, resulting in nearly 181,000 deaths. The gold standards for the diagnosis are either direct microscopic identification or fungal cultures. However, these diagnostic methods need special types of equipment and clinical expertise, and relatively low sensitivities have also been reported. This study aims to produce and implement a deep-learning approach to detect C. neoformans in patient samples. Therefore, we adopted the state-of-the-art VGG16 model, which determines the output information from a single image. Images that contain C. neoformans are designated positive, while others are designated negative throughout this section. Model training, validation, testing, and evaluation were conducted using frameworks and libraries. The state-of-the-art VGG16 model produced an accuracy and loss of 86.88% and 0.36203, respectively. Results prove that the deep learning framework VGG16 can be helpful as an alternative diagnostic method for the rapid and accurate identification of the C. neoformans, leading to early diagnosis and subsequent treatment. Further studies should include more and higher quality images to eliminate the limitations of the adopted deep learning model.

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Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears

Cited by 13 publications

References 19 publications

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model

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