Pair-wise discrimination of some lung diseases using chest radiography

Noor, Norliza Mohd; Rijal, Omar Mohd; Ashari, Yunus; Mahayiddin, Aziah Ahmad; Peng, Gan Chew; Ling, Ong Ee; Bakar, Syed Abu

doi:10.1109/tenconspring.2014.6863015

Cited by 5 publications

(2 citation statements)

References 11 publications

(9 reference statements)

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“…Each cell chooses an action from its possible action vector, after which, based on the selected actions, a pattern sends response signal to the lattice. Noor et al (2014) [7] have proposed a novel texturebased statistical discrimination procedure using chest Alam & Hossan (2018) [10] have proposed an algorithm which can predict lung cancer and gives extremely encouraging outcomes in contrast to other techniques. A set of textural features extracted from the separated Region of Interests (ROIs) is classified using SVM.…”

Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

Mercina¹,

Madhumathi²,

Priyanga³

et al. 2019

IJSRCSEIT

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Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

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Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

Mercina¹,

Madhumathi²,

Priyanga³

et al. 2019

IJSRCSEIT

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show abstract

“…In 2011, Karargyris et al provided a segmentation-based method for screening Pneumonia and Tuberculosis while using chest X-rays [20]. In 2014, Ebrahimian et al proposed a solution to the tricky problem of discriminating Pulmonary Tuberculosis (PTB) and Lobar Pneumonia (PNEU) [21], Noor et al described a method for distinguishing among PNEU, PTB, and LC using chest radiographs, where they extracted features using wavelet texture measures and principal component analysis (PCA) [22], and Abiyev et al used Convolutional Neural Networks (CNN) for predicting the presence of chest disease through analyzing chest radiographs [23]. In 2016, Khobragade et al designed an automatic detection scheme to detect lung diseases-TB, LC, and Pneumonia-by segmenting the images of lungs from chest radiographs, and then using Artificial Neural Network (ANN) for classification [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Nahid

Sikder

Razzaque

et al. 2020

Sensors

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Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

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Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection

et al. 2020

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Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)—CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.

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Pair-wise discrimination of some lung diseases using chest radiography

Cited by 5 publications

References 11 publications

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection

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