ABSTRACT

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.

摘要

最近几天，COVID-19 大流行影响了全球许多人的生活，需要进行大量筛查测试才能检测出冠状病毒的存在。同时，深度学习 (DL) 概念的兴起有助于有效开发 COVID-19 诊断模型，从而以最少的计算时间获得最大的检测率。本文介绍了一种新的基于残差网络 (ResNet) 的类注意层和双向 LSTM，称为 RCAL-BiLSTM，用于 COVID-19 诊断。所提出的 RCAL-BiLSTM 模型涉及一系列过程，即基于双边滤波 (BF) 的预处理、基于 RCAL-BiLSTM 的特征提取和基于 softmax (SM) 的分类。一旦 BF 技术生成预处理图像，基于 RCAL-BiLSTM 的特征提取过程将使用三个模块进行，即基于 ResNet 的特征提取、CAL 和 Bi-LSTM 模块。最后，应用 SM 层将特征向量分类为相应的特征图。所提出的 RCAL-BiLSTM 模型的实验验证是针对胸部 X 射线数据集进行测试的，结果是在几个方面确定的。实验结果指出 RCAL-BiLSTM 模型的优越性，最大灵敏度为 93.28%，特异性为 94.61%，精确度为 94.90%，准确度为 94.88%，F 分数为 93.10%，kappa 值为 91.40%。