ACRnet: Adaptive Cross-transfer Residual neural network for chest X-ray images discrimination

Mapping Intimacies ◽

10.21203/rs.3.rs-1015912/v1 ◽

2021 ◽

Author(s):

Wenyu Zhang

Keyword(s):

Neural Network ◽

Recognition Performance ◽

Reference Value ◽

Experimental Result ◽

Generative Adversarial Network ◽

X Ray ◽

Residual Structure ◽

Original Dataset ◽

Recognition Ability ◽

Chest X Ray

Abstract Background: Cardiothoracic diseases are a serious threat to human health and chest X-ray images have great reference value for the diagnosis and treatment. However, it is difficult for professional doctors to accurately diagnose cardiothoracic diseases through chest X-ray images sometimes and there will be different understanding based on human subjective differences, which will affect the judgment and treatment of diseases. Therefore, it is very necessary to develop a high-precision neural network to recognize chest X-ray images of cardiothoracic diseases.Methods: In this work, we cross-transfer the information extracted by the residual block and by the adaptive structure to different levels, which avoids the reduction of the adaptive function by residual structure and improves the recognition performance of the model. To evaluate the recognition ability of ACRnet, VGG16, InceptionV2, ResNet101 and CliqueNet are used for comparison. In addition, we use the deep convolution generative adversarial network (DCGAN) to expand the original dataset.Result: We find ACRnet has better recognition ability than other networks in identifying cardiomegaly, emphysema and normal. Besides, ACRnet's recognition ability has been greatly improved after data expansion. Conclusions: The experimental result indicates that emphysema and cardiomegaly can be effectively identified by ACRnet. Using DCGAN technology to expand the dataset can further improve the recognition ability of the model.

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A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network

Traitement du signal ◽

10.18280/ts.380309 ◽

2021 ◽

Vol 38 (3) ◽

pp. 619-627

Author(s):

Kazim Firildak ◽

Muhammed Fatih Talu

Keyword(s):

Classification Accuracy ◽

Data Augmentation ◽

Model Parameters ◽

Classification Problems ◽

Generative Adversarial Network ◽

X Ray ◽

Discrimination Capability ◽

Adversarial Network ◽

Original Dataset ◽

Chest X Ray

Pneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle’s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.

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Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽

10.1371/journal.pone.0242899 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242899

Author(s):

Musatafa Abbas Abbood Albadr ◽

Sabrina Tiun ◽

Masri Ayob ◽

Fahad Taha AL-Dhief ◽

Khairuddin Omar ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Support Vector Machine ◽

Ct Scan ◽

Extreme Learning Machine ◽

Experimental Result ◽

Support Vector ◽

X Ray ◽

Chest X Ray ◽

Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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