A Semi-Supervised Approach to Semantic Segmentation of Chest X-Ray Images Using DEEPLABV3 for COVID19 Detection (original) (raw)
2020
Abstract
In the current situation, COVID19 is one of the life-threatening respiratory infections which infect humans as well as animal species. The early and accurate detection of COVID19 is essential to make proper decisions and to ensure recovery treatment for patients which will help to save patientÂ’s lives. Deep Learning approaches are successfully used to analyse and detect COVID19 on chest X-ray and CT scan images. In this paper, a semi supervised learning approach is used to segment the covid affected region, using DeepLabV3 from Chest X-ray (CXR), and the ground truths for the segmentation is created by pre-processing the output class activation maps of DenseNet201, which is used for multiclass classification of COVID19 and non COVID19 X-rays. The performance of the model is evaluated by varying the optimization function, number of training epochs, scheduling learning rates.
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