Chest X Ray In Clinical Practice


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The model can determine the general nodular area but cannot determine the exact locations of the nodules. Although advanced features can be derived from classical deep learning models that used transfer learning, they are not related to medical image analysis tasks. The greater the gap between features extracted from natural images and those from medical images implies lower transferability of the feature.

Wang et al.


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  7. Bibliographic Information.

With the guidance of the specific false positives, i. The low sensitivity is likely due to hand-crafted features being not superior. Detecting tuberculosis in CXRs is a difficult task since it has different manifestations. Some studies detected tuberculosis based on the shape, texture, and local characteristics of the lungs, focusing on the general performance.

To imitate radiologists for visual detection and diagnosis of the texture features of chest X-ray images, Rohmah et al. The results showed that tuberculosis can be detected based on the statistical features in the image histogram. Tan et al. Noor et al. They first applied the wavelet transform to the CXR image, calculated 12 texture measures from the wavelet coefficients, reduced the dimensions with PCA, and estimated the probability of misclassification using the probability ellipsoid and discriminant functions.

In addition to extracting texture features, some studies applied bone suppression to pretreat chest radiographs for improving the classification performance. Leibstein et al. Maduskar et al. Hogeweg et al. The combination of texture anomaly detection and clavicle detection reduced false positives. In a study [ 86 ], the authors fused the supervisory subsystems for detecting the texture, shape, and focal abnormalities and developed a generic framework for tuberculosis detection.

Another portion of the literature has focused on the detection of specific manifestations, such as diffuse opacity, effusion, cavities, and nodule lesions. Song et al. These investigators studied the initial extraction of rib threads. After locating the ribs, morphological opening operations and seed growth methods were used to automatically locate the focal opacity.

However, handling images that have blots or have no visible features on the border is not sufficient and can even lead to misjudgment. Shen et al. The gradient inverse coefficient of variation GICOV describes the texture area boundary , and the circular measure describes the shape of the latent cavity. This method is the first automatic algorithm that detects tuberculosis accurately but uses a global adaptive threshold in such a way that automatic initialization cannot place the initial contour within the cavity, leaving a cavity.

Xu et al. These candidates were then further refined using Hessian matrix eigenvalues and snake-based techniques by means of active contouring. In the final phase, SVM was used to reduce the false positives by further narrowing the enhanced cavity candidates at finer scales. Most prior CAD algorithms used well-designed morphological features to distinguish different types of lesions and to improve the screening performance.

However, such manual features do not guarantee the best description of tuberculosis classification. Recently, the role of deep learning in tuberculosis classification has proven to be effective.

Hwang et al. Lakhani et al. The interstitial lung is support tissue outside the alveolar and terminal airway epithelium. When the interstitial lung is damaged, the chest radiograph indicates changes in the texture of the lung [ 92 ], such as linear, reticular, nodular, honeycomb, etc. Interstitial lung disease ILD is a group of basic pathological lesions with diffuse pulmonary parenchyma, alveolar inflammation, and interstitial fibrosis, including interstitial pulmonary edema, allergic pneumonia, idiopathic pulmonary interstitial fibrosis, sarcoidosis, and lung lymphatic cancer [ 94 , 95 ].

Cases with different interstitial lesions behave very similarly on light sheets, even for professionals, it is difficult to distinguish between normal and non-normal tissue based on texture. Therefore, the detection of ILD in chest radiography is one of the most difficult tasks for radiologists.

Then, pretrained NNs were used to classify suspicious areas to be detected. This system can help doctors improve the accuracy of interstitial lesion detection. Plankis et al. This approach can detect a variety of pathological features of interstitial lung tissue based on an active contour algorithm which can select the lung region. The region is then divided into 40 different regions of interest. Then, a two-dimensional Daubechies wavelet transform is performed on the ROI to calculate the texture measure.

However, with the extensive application of deep learning in the detection of lung diseases, there is little literature on the detection of interstitial lung disease in the absence of a large chest X-ray dataset on ILD. In chest X-rays, in addition to pulmonary nodules, tuberculosis, and ILD, there are other diseases that can be detected, such as cardiomegaly, pneumonia, pulmonary edema, and emphysema. There is less literature on these diseases, and a brief discussion is given here. Detecting cardiomegaly usually requires analyzing the heart size and calculating the cardiothoracic ratio CTR and developing a cardiac tumor screening system.

An EASY way to read the Chest X Ray!

Candemir et al. Islam et al. Pneumonia and pulmonary edema can be classified by extracting texture features. Parveen et al. The results showed that the lung area of the chest was low in black or dark gray. When a patient has pneumonia, the lungs are full of water or sputum.

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Thus, there will be more absorbed radiation, and the lung areas will be white or light gray. This approach can help doctors detect the degree of infection easily and accurately. Kumar et al. Here, they did not use large datasets and validate other lung conditions. There may be one or more diseases in the chest radiographs. This section discusses the methods of using CAD technique to detect multiple diseases. Avni et al. However, their algorithm was designed only for global representation and could identify the diseases manifested in the locally or relatively small regions.

Each ROI was transformed into four subsets using a two-dimensional Daubechies wavelet transform, which represented the trend, horizontal, vertical, and diagonal detail coefficients. Twelve types of texture measurements, such as the mean energy, entropy, contrast, and maximum column total energy, were calculated. The modified principal component ModPC method was used to generate the feature vectors for the discrimination process. This algorithm is different from other semi-automatic methods in that the ROI choice does not involve the usual segmentation problem, and the proposed statistical-based CAD algorithm does not rely on establishing precise boundaries and avoids the possibility of losing information from the original image.

However, this method still requires further work that involves larger samples for validation studies. However, the detection was performed with features learned from non-medical datasets, and it was inaccurate. Cicero et al. It was shown that the current CNN architecture can be trained with a medical dataset of moderate size, which solves the problem of simultaneous prediction of multiple labels for the detection and removal of common diseases in chest radiographs.

NIH [ 23 ] published a large-scale chest X-ray dataset and used a weak-supervised multi-label method to classify and locate eight diseases, which validated the usability of deep learning on this dataset. Based on this result, Yao et al. Rajpurkar et al. These methods of detecting multiple labels in chest radiographs all input the global image into the network. However, the lesion area can be very small compared to the global image, and using a global image for classification could result in a significant amount of noise outside the lesion area. In response to this problem, Guan et al.

This network combines global and local information to improve the recognition performance. This study discussed various CAD algorithms for detecting abnormal chest radiographs.

References

CXR CAD algorithms have wide applications in detecting various diseases, and they are playing a vital role as a second opinion for medical experts. In this section, we will discuss and compare the CAD algorithms in detection of chest abnormalities, and will state some problems and the future works in this field. From the literatures, it can be found that there are many CAD methods currently used to detect abnormalities in chest radiographs. Most of these methods belong to the field of artificial intelligence [ ] and they dedicate to computer-aid detection based on the chest radiograph.

This should be a main direction of computer-assisted detection of chest radiographs in the future. We think that deep learning has very far-reaching development potential, while at the same time it needs further improvement for playing a greater role.

At present, deep learning methods classify diseases by extracting the features from the limited datasets. However, the problems with these methods are that limited datasets have limitations, such as unbalanced sample distribution, and the generality of the networks trained with them is insufficient. In order to further improve the classification ability, the following aspects should be carried out: 1 Dataset: Based on existing datasets, a more representative dataset with a larger number of examples, preferably from different devices and different regions, is established, making the training network more versatile.

X-rays | healthdirect

DENSENET connected all layers with matching feature-map sizes directly with each other to ensure maximum information flow between layers in the network. They can farther alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters [ ]. Dual Path Network [ ] combined residual channels with densely connected paths to increase training speed significantly, reduce memory footprint, and maintain higher accuracy. Similar to the literature [ ], the Residual Attention Network [ ] introduced an attention mechanism to extract the significant features from the images by stacking multiple attention modules.

These mentioned above methods have greatly improved the classification accuracy. The big data is used in current deep learning methods to extract the features of the corresponding disease through the convolution algorithm, that is, to extract different features from shallow level to deep level through convolution operations. The training process of the neural network makes the entire network to automatically adjust the parameters of the convolution kernel, resulting in the suitable classification features being consistent with the appearance of the chest radiograph image.

Although these methods have made great progress in this area, it is very time-consuming to build big data. Therefore, it should be considered whether future studies can use other domain datasets to emulate chest radiographs.

Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice
Chest X Ray In Clinical Practice Chest X Ray In Clinical Practice

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