Abstract
Point of Care Ultrasound (PoCUS) imaging is an important tool in detecting lung consolidations and tissue sliding, and hence has a potential to identify the onset of novel-CoVID-19 attack in a person. Of late, Convolutional Neural Network (CNN) architectures have gained popularity in improving the accuracy of the predictions. Motivated by this, in this paper, we introduce a CNN based Auto Encoder (AE-CNN) for a better representation of the features to get an accurate prediction. While most of the existing models contain ‘fully connected’ (FC) layers, in our work, we use only convolutional layers instead of FC layers before the output layer, which helps us in achieving a less training time of the model. Moreover, fully connected layers of a network can not learn the patterns in an image as much as convolutional layers can. This is the main advantage of our model over its existing counterparts. We demonstrate that our model detects the lung abnormalities in the ultrasound images with an accuracy of 96.6%.
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Chandra, G., Challa, M.R. (2021). AE-CNN Based Supervised Image Classification. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1378. Springer, Singapore. https://doi.org/10.1007/978-981-16-1103-2_36
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DOI: https://doi.org/10.1007/978-981-16-1103-2_36
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