Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography

Ho, Thi Kieu Khanh; Gwak, Jeonghwan

doi:10.3390/app9194130

Cited by 76 publications

(47 citation statements)

References 53 publications

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“…Rajpurkar et al [42] used CheXNeXt, a very deep CNN with 121 layers, to detect 14 different pathologies, including pneumonia, in frontal-view chest X-rays. A localization approach based on pre-trained DenseNet-121, along with feature extraction, was used to identify 14 thoracic diseases in [43]. Saraiva et al [44], Ayan et al [45], and Rahman et al [46] used deep learning based methods for pneumonia classification.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…Wang et al, 2017 ). Moreover, the DL approach requires huge computational resources along with a large number of accurately annotated CXR images to train the model, which restrain its clinical acceptability ( Altaf et al, 2019 , Ho and Gwak, 2019 ). Conventional ML techniques can be better integrated with CAD systems to overcome these shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

241

190

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Highlights Proposed automatic COVID screening (ACoS) system for detection of infected patients. Random image augmentation is applied to incorporate the variability in the images. Applied hierarchical (two phase) classification to segregate three classes. Majority vote based classifier ensemble is used to combine model’s prediction. Proposed method show promising potential to detect nCOVID-19 infected patients.

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“…PNN was used in the classification of the nodules. Ho et al [6] opted to use feature concatenation for the efficient classification of 14 thoracic diseases. In their study, four local texture descriptors, namely SIFT, GIST, LBP, and HOG, were concatenated.…”

Section: Introductionmentioning

confidence: 99%

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Ismael

Şengür

2020

Health Inf Sci Syst

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COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

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Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography

Cited by 76 publications

References 53 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

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