A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks

Sarkar, Rahul; Hazra, Animesh; Sadhu, Koulick; Ghosh, Preetam

doi:10.1007/978-981-13-8798-2_1

Cited by 18 publications

(11 citation statements)

References 7 publications

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“…The slight dip in both [8] and proposed Ensemble results can be attributed to low training image resolution which affects the classification performance of the CNN. This has been supported and reasoned in the works of Sarkar et al in [28], Koziarski et al in [29], Dodge et al in [30] and Kannojia et al in [31]. The work in [29], reports that models that achieved high accuracy on the original, undistorted images were also more resilient to low image resolution and the pattern was observed across almost all the architectures.…”

Section: B Results and Discussionmentioning

confidence: 52%

Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Suryaa¹,

R²,

Aiswarya³

2021

IJACSA

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Section: B Results and Discussionmentioning

confidence: 52%

Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Suryaa¹,

R²,

Aiswarya³

2021

IJACSA

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“…A wide array of research works uncovers the discriminatory information that best expresses pneumonia from normal samples on chest X-rays. The methods employed in the research of pneumonia/COVID-19 classification from chest X-rays fall into these categories: Machine learning (ML) methods [11] , [12] , [13] , statistical approaches [14] , CNN architectures [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , transfer learning [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , complex CNN models [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] and adversarial networks [43] .…”

Section: Related Workmentioning

confidence: 99%

“…Sarkar et al. employed residual CNN with separable convolutions for pneumonia detection from X-rays [37] . Jaiswal et al.…”

Section: Related Workmentioning

confidence: 99%

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN

Karthik

Menaka

Hariharan

2021

Applied Soft Computing

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COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN’s prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

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“…They trained the CNN to classify the input X-ray image into normal or pneumonic class. The accurate and efficient pneumonia detection from the input chest X-ray images was presented in Sarkar et al ( 2020 ). They first pre-processed the input X-ray image using bilateral filtering and contrast enhancement techniques.…”

Section: Related Workmentioning

confidence: 99%

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

Goyal

Singh

2021

J Ambient Intell Human Comput

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Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.

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A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks

Cited by 18 publications

References 7 publications

Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

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