Implementation of 1D-Convolution Neural Network for Pneumonia Classification Based Chest X-Ray Image

Fathurahman, Muhamad; Fauzi, Sri Cahya; Haryanti, Sri Chusri; Rahmawati, Ummi Azizah; Suherlan, Elan

doi:10.1007/978-3-030-36056-6_18

Cited by 5 publications

(3 citation statements)

References 7 publications

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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%

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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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Section: Related Workmentioning

confidence: 99%

“…Fathurahman et al. used the Histogram of Oriented Gradient (HOG) and GCLM features extracted from the chest X-ray to train a one-dimensional convolutional network [17] . Nakrani 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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“…The easily transmitted COVID-19 has first risen in “Wuhan, China, in Dec 2019” (Nakrani et al , 2020) that universally damages the finance, health and personality of the territories and countries in the world. The COVID-19 patient undergoes many health difficulties such as infections and severe respiratory disorders (Agyekum et al , 2021; Fathurahman et al , 2019). The primary treatments along with the preliminary diagnosis are the steps to decrease the death rate in severe cases.…”

Section: Introductionmentioning

confidence: 99%

Adaptive deep learning for deep COVID-19 diagnosis

S.¹,

M.K.²

2022

JEDT

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Purpose COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model. Design/methodology/approach The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets. Findings From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques. Originality/value This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

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Deep Learning Based Pneumonia Infection Classification in Chest X-ray Images Using Convolutional Neural Network Model

Nayak

Sahu

2022

Lecture Notes in Electrical Engineering

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Implementation of 1D-Convolution Neural Network for Pneumonia Classification Based Chest X-Ray Image

Cited by 5 publications

References 7 publications

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

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

Adaptive deep learning for deep COVID-19 diagnosis

Deep Learning Based Pneumonia Infection Classification in Chest X-ray Images Using Convolutional Neural Network Model

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