Detection of Novel Coronavirus from Chest X-Ray Radiograph Images via Automated Machine Learning and CAD4COVID

Izdihar, K.; Karim, Muhammad Khalis Abdul; Aresli, N. N; Radzi, Siti Fairuz Mat; Sabarudin, Akmal; Yunus, Mardhiyati Mohd; Rahman, Mohd Amiruddin Abdul; Shamsul, S

doi:10.1109/icoten52080.2021.9493542

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Automated machine learning (AutoML) is a tool that automates the process of developing a machine learning model that has lately gained popularity. AutoML libraries come in a variety of environment including Auto-WEKA [ 29 ], Tree-based Pipeline Optimisation Tool (TPOT) [ 30 , 31 ], Auto-Sklearn, and others [ 32 , 33 , 34 ]. In this context, AutoML runs through a dataset and suggests the most optimum algorithm with the parameters set.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

et al. 2022

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Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.

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Section: Introductionmentioning

confidence: 99%

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

et al. 2022

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“…A future research direction would be to use the other ML models on more and various real datasets. In 2021, Izdihar et al [30], the AutoML method; study collected 70 chest xray (CXR) photos from a hospital in Kuala Lumpur to investigate the sensitivities of detection and evaluate the accuracy of the classifier performance. TPOT has an accuracy result of 0.83, and K-NN was chosen as the best pipeline.…”

Section: Related Workmentioning

confidence: 99%

An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection

Maher

Yousif

2023

IJ-AI

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The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.

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“…The component of radiomic represents high quantitative image features of tumor phenotypes that characterize the volumes of interest. The feature extraction contains information from input images and represents data in lower dimensional space [ 7 , 8 , 9 ]. This involves a complex mathematical algorithm which describes phenotypes of tumors that are unrecognized and might not be detectable by human observation.…”

Section: Introductionmentioning

confidence: 99%

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

et al. 2022

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Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.

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Detection of Novel Coronavirus from Chest X-Ray Radiograph Images via Automated Machine Learning and CAD4COVID

Cited by 5 publications

References 5 publications

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

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