Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter; Thompson, Matthew M.; Sayers, Rob; Bown, Matthew J.; Choke, E.; Ma, Xianghong

doi:10.1177/0954411917731592

Cited by 37 publications

(22 citation statements)

References 69 publications

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“…K-NN is a conventional non-parametric classifier. Despite its simplicity, K-NN has the power to accomplish high classification results in medical applications [45,46]. It allocates a class value to each data point in the training set by investigating the class values of its K nearest neighbors [47].…”

Section: Methodsmentioning

confidence: 99%

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

2019

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Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.

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Section: Methodsmentioning

confidence: 99%

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

2019

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“…17 . This is because the MCS usually enhances the classification accuracy ( Attallah et al, 2017b ; Krawczyk & Schaefer, 2014 ; Fusco et al, 2017 ; Fusco et al, 2012 ; Melekoodappattu & Subbian, 2020 ). Moreover, the fusion with the AE and MCS in the second fusion stage reduced both the fused feature dimension and the execution time for classification for both datasets as shown in Table 10 .…”

Section: Discussionmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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“…The MCS can avoid the possibility of poor results that are generated from a certain unsuitably selected model. This is equivalent to medical applications in cases where diagnosis to a specific illness is made by taking decisions from various doctors to come to a more confident final decision [35].…”

Section: Multiple Classifier System (Mcs)mentioning

confidence: 99%

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

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Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

Cited by 37 publications

References 69 publications

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

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