Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network

Khobragade, Shubhangi; Tiwari, Aditya; Patil, Chetan; Narke, Vikram

doi:10.1109/icpeices.2016.7853683

Cited by 87 publications

(42 citation statements)

References 4 publications

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“…The diagnosis of chronic obstructive pulmonary and pneumonia diseases was implemented using neural networks and artificial immune system [ 8 ]. In [ 9 ], the detection of lung diseases such as TB, pneumonia, and lung cancer using chest radiographs is considered. The histogram equalization in image segmentation was applied for image preprocessing, and feedforward neural network is used for classification purpose.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

268

118

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Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

268

118

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“…It is found that Canny edge detection has more accuracy compared to Sobel and the computation time is also lesser than Sobel. Table II shows the comparison of all the classifiers namely SVM, Adaboost, KNN, Neural Network [12], and Decision Tree in terms of Classification Accuracy, sensitivity, specificity, AUC, F measure, Precision, Brier Score, Matthews Correlation Coefficient.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

Sebastian*,

Devi

2020

IJITEE

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Lung cancer is one of the very deadly diseases in the world. However, diagnosing it at an early stage and treating it properly can protect lives. Although Computer Tomography (CT) scan imaging is one of the fruitful imaging in the field of medicine, it is the hardest for clinicians to clarify and recognize cancer from those images. And it is carried out with Mumford and Shah functional model, and support vector machine (SVM) classifier. Also, the system takes less computation time and thus, is highly efficient than existing algorithms which grab 98% accuracy. Further, the performance analysis of the proposed system is executed using seven assessment metrics namely Classification Accuracy, sensitivity, specificity, AUC, F measure, Precision, Brier Score, MCC. Finally, the results of SVM are compared with KNN, Decision-Tree, and Adaptive Boosting algorithms in terms of the seven metrics. The results show that there is significant progress in the above measures than the existing method.

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“…For the model interpretability, the visualization techniques 22–24 were developed to concentrate on the important regions on the image by overlaying with the weighted sum of feature maps from a specific layer. In comparison with traditional machine‐learning approaches, 25,26 the more advanced deep‐learning methods have exhibited promising performance on diagnoses of lung diseases based on chest radiographs, such as the classification of pulmonary tuberculosis, chest pathology identification, and pneumonia detection 27–29 …”

Section: Introductionmentioning

confidence: 99%

Image‐based deep learning in diagnosing the etiology of pneumonia on pediatric chest X‐rays

Zhao

Liu

et al. 2021

Pediatric Pulmonology

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Purpose Comparing the efficacy of a deep‐learning model in classifying the etiology of pneumonia on pediatric chest X‐rays (CXRs) with that of human readers. Methods We built a clinical‐pediatric CXR set containing 4035 patients to exploit a deep‐learning model called Resnet‐50 for differentiating viral from bacterial pneumonia. The dataset was split into training (80%) and validation (20%). Model performance was assessed by receiver operating characteristic curve and area under the curve (AUC) on the first test set of 400 CXRs collected from different studies. For the second test set composed of 100 independent examinations obtained from the daily clinical practice at our institution, the kappa coefficient was selected to measure the interrater agreement in a pairwise fashion for the reference standard, all reviewers, and the model. Gradient‐weighted class activation mapping was used to visualize the significant areas contributing to the model prediction. Results On the first test set, the best‐performing classifier achieved an AUC of 0.919 (p < .001), with a sensitivity of 79.0% and specificity of 88.9%. On the second test set, the classifier achieved performance similar to that of human experts, which resulted in a sensitivity of 74.3% and specificity of 90.8%, positive and negative likelihood ratios of 8.1 and 0.3, respectively. Contingence tables and kappa values further revealed that expert reviewers and model reached substantial agreements on differentiating the etiology of pediatric pneumonia. Conclusions This study demonstrated that the model performed similarly as human reviewers and recognized the regions of pathology on CXRs.

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Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network

Cited by 87 publications

References 4 publications

Deep Convolutional Neural Networks for Chest Diseases Detection

Deep Convolutional Neural Networks for Chest Diseases Detection

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

Image‐based deep learning in diagnosing the etiology of pneumonia on pediatric chest X‐rays

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