COVID-19 prediction using CNN Algorithm

Abstract : Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artifacts in a weakly-supervised way. Furthermore, we introduce a new method based on uniforms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.
 EXISTING SYSTEM :
 ? Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. ? Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. ? We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies.
 DISADVANTAGE :
 ? CT scanning is expensive and highly irradiating, carries significant risk of cross infection to healthcare workers and requires extensive, time consuming sterilization. ? We address the problem of automatic localization of pathological artifacts evaluating the performance of state-of-the-art semantic segmentation methods derived from fully convolutional architectures. ? While this problem can trivially be cast within a classification framework, in this paper we argue that ordinal regression is more appropriate as we are interested in predicting labels from an ordinal scale.
 PROPOSED SYSTEM :
 • We propose a frame-based model that correctly distinguishes COVID-19 LUS Images from healthy and bacterial pneumonia data with a sensitivity of 0.90 0.08 and a specificity of 0.96 0.04 using CNN Algorithm. • To investigate the utility of the proposed CNN method, we employ interpretability methods for the spatiotemporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. • Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. • Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). • The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for Covid-19 diseases.
 ADVANTAGE :
 ? In this paper, we present all results omitting the uninformative class, as it is not relevant for the analysis of differential diagnosis performance and would bias the results, that is, lead to a higher classification accuracy for the uninformative class. ? The max argmax hard rule is strongly biased towards predicting the highest score, resulting in bad performance on all other scores. ? The replacement of the traditional cross-entropy (CE) with the SORD loss for ordinal regression clearly improves the performance.

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