COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

Zulkifley, Mohd Asyraf; Abdani, Siti Raihanah; Zulkifley, Nuraisyah Hani

doi:10.3390/sym12091530

Cited by 46 publications

(35 citation statements)

References 38 publications

(57 reference statements)

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“…These four points are selected since they represent the outer points of our region of interest, which is the palm region. Several recent key-points detectors that are based on convolutional neural networks have been tested and validated that include ResNet-50 [ 58 ], GoogleNet [ 59 ], ShuffleNet V1 [ 60 ], ShuffleNet V2 [ 61 ], MobileNet V1 [ 62 ], MobileNet V2 [ 63 ], MobileNet V3 [ 64 ], SqueezeNet [ 65 ], LightCovidNet [ 8 ], Xception [ 66 ], DenseNet [ 67 ], and SPPCovidNet [ 7 ]. The coordinates for these four key-points are denoted as

, where

is the Cartesian coordinate with respect to the origin that is set at the top-left point of the image.…”

Section: Methodsmentioning

confidence: 99%

“…A recently published article by Guo et al [ 6 ] has mentioned that an estimated 76% of radiologists have utilized hand X-ray images to assess the bone age. Moreover, X-ray images is a popular screening modality that has been widely used to screen various diseases like COVID-19 [ 7 , 8 ], pneumonia [ 9 , 10 ], osteoarthritis [ 11 , 12 ], dental decay [ 13 , 14 ], and many more. In addition, X-ray images have also been used as an imaging modality in astronomy to investigate the X-ray emission from celestial objects due to its advantages such as simplicity and high-speed [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

et al. 2021

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Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

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, where

is the Cartesian coordinate with respect to the origin that is set at the top-left point of the image.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

et al. 2021

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“…The residual convolution network ... V for the middle module will be using the same number of filters for all three separable convolutions, which is contrary to the popular approach in [41], where a bottleneck scheme is used with the middle layer will have the most number of filters. The residual or skip connection will maintain the same size as its input, which will be combined with the main branch using the addition operator, as shown in Figure 8.…”

Section: Xception-41 Regressormentioning

confidence: 99%

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

2020

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One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

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“…The research by [54] focuses only on the screening stage. The synthetic data, which are generated by a conditional deep convolutional generative adversarial network (conditional DC-GAN), is used to augment the training dataset for COVID-19 classification.…”

Section: Related Workmentioning

confidence: 99%

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

et al. 2021

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In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%.

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COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

Cited by 46 publications

References 38 publications

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

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