Random Forest Based Classification of Medical X-Ray Images Using a Genetic Algorithm for Feature Selection

Nedjar, Imane; Daho, Mostafa El Habib; Settouti, Nesma; Mahmoudi, Saïd; Chikh, Mohammed Amine

doi:10.1142/s0219519415400254

Cited by 21 publications

(15 citation statements)

References 8 publications

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“…RF outperformed other classifiers on both the training and test set, which is consistent with its popularity among many previous medical image analysis studies [ 33 , 34 , 35 , 36 ]. Several features of RF may contribute to its higher performance on medical images—RFs are suited for high predictor dimension relative to sample size, they inherently perform feature selection, and they generalize well to regions of the feature space with sparse data [ 34 , 35 ].…”

Section: Discussionsupporting

confidence: 84%

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

Wang

Wood

Gao

et al. 2021

Entropy

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The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.

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Section: Discussionsupporting

confidence: 84%

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

Wang

Wood

Gao

et al. 2021

Entropy

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“…We implemented a number of traditional machine learning methods including linear discriminant analysis (LDA), support vector machines (SVM), multilayer perceptron neural networks (MLP), random forest (RF) and gradient boosting (XGB) via XGBoost [89]. These machine learning methods have demonstrated effectiveness with the semantic segmentation of images across a number of disciplines including bioimage analysis [87,[90][91][92][93][94][95][96] and remote sensing [97][98][99][100][101][102]. Notably, classifier models trained with ensemble methods such as random forest and gradient boosting benefit from a relatively high level of interpretability [103][104][105] as the individual estimators (decision trees) within these models can be understood and interpreted without significant effort.…”

Section: Non-deep Machine Learning Methodsmentioning

confidence: 99%

“…We first implemented random forest and gradient boosting via XGBoost [71], two ensemble machine learning methods that have proven effective with the semantic segmentation of images across a number of disciplines including bioimage analysis [72][73][74][75][76][77][78][79] and remote sensing [80][81][82][83][84][85]. Unlike neural networks and deep learning methods, models trained with these algorithms benefit from simplicity and a relatively high level of interpretability [86][87][88].…”

Section: Random Forest and Gradient Boostingmentioning

confidence: 99%

Deep learning classification of lipid droplets in quantitative phase images

Sheneman

Stephanopoulos

Vasdekis

2020

Preprint

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We report the application of supervised machine learning to the automated classification of lipid-droplets in label-free, quantitative-phase images. By comparing various machine learning methods that are publicly available and commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, the implementation of all machine learning methods that we compared, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent replacement candidate of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism at the single-cell level.

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“…This technique is a collection of classification and regression trees [101]. Here, a forest of classification trees is generated, where each tree is grown on a bootstrap sample of the data [102]. In that way, the RF classifier consists of a collection of binary classifiers where each decision tree casts a unit vote for the most popular class label (see Figure 9d) [103].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Random Forest Based Classification of Medical X-Ray Images Using a Genetic Algorithm for Feature Selection

Cited by 21 publications

References 8 publications

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

Deep learning classification of lipid droplets in quantitative phase images

MR Images, Brain Lesions, and Deep Learning

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