Computational Intelligence and Healthcare Informatics. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Программы
Год издания: 0
isbn: 9781119818694
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pathology detection not only restricted from CXR images but can also be done from video data of lung sonography. Deep learning approach for detection of COVID-19–related pathologies from Lung Ultrasonography is developed in [51]. By applying the facts that the augmented Lung Ultrasound (LUS) images improve the performance of network [62] in detecting healthy and ill patient and keeping consistencies in perturbed and original images, hence robust and more generalized network can be constructed [52, 55]. To do so, Regularized Spatial Transformer Network (Reg-STN) is developed. Later, CNN and spatial transformer network (STN) are jointly trained using ADAMS optimizer. Network lung sonography videos of 35 patients from various clinical centers from Italy were captured and then divided into 58,924 frames. The localization of COVID-19 pathologies were detected through STN which is based on the concept that the pathologies are located in a very small portion of image therefore no need to consider complete image.

      A three-layer Fusion High Resolution Network (FHRNet) has been applied for feature extraction and fusion CNN is adopted for classifying pathologies in CXR is presented in [26]. FHRNet helped in reducing noise and highlighting lung region. Moreover, FHRN has three branches: local feature extraction, global feature extraction, and feature fusion module wherein local and global feature extraction network finds probabilities of one of the 14 classes. Input given to local feature extractor is a small lung region obtained by applying mask generated from global feature extractor. Two HRNets are adjusted to obtain prominent feature from lung region and whole image. HRNet is connected to global feature extraction layer through feature fusion layer having SoftMax classifier at the end which helps in classifying input image into one of the 14 pathologies. Another deep CNN consisting of 121 layer is developed to detect 5 different chest pathologies: Consolidation, Mass, Pneumonia, Nodule, and Atelectasis [43] entropy, as a loss function is utilized and achieved better AUC-ROC values for Pneumonia, Nodule, and Atelactasis than the model by Wang et al. [70].

      1 Model parameters: type of model used, input image size, number of layers epoch, loss function used, and accuracy.

      2 Accuracy achieved for all 14 different pathologies and dataset used for experimentation.

      3 Other metrics: model used, specificity, sensitivity, F1-score, precision, and type of pathology detected.

      4 On the basis of hardware and software used and input image size.

Ref. Model used Dataset No. layers Epoch Activation function Iterations Pathology detected
[23] DenseNet-121 ChestX-ray14 121 - Softmax 50,000 14 chest pathologies
[67] Pretrained CNNs: ChestX-ray14

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