A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Abstract

Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

“… [37] Encoder-Decoder CNN 0.636 0.457 0.209 0.206 5 Naeem et al. [30] CNN-LSTM autoencoder on SIFT, GIST features 0.684 0.441 0.195 0.190 6 Zhou et al [41] Spatial channel attention residual network 0.691 0.437 0.191 0.188 7 Mohammed et al. [42] Spatial channel attention CNN-LSTM 0.720 0.427 0.183 0.182 8 Chatzitofis et al.…”

“…CNN-LSTM based models generate an optimal level of fit with better results [30] , [42] . Specifically, Naeem et al.…”

“…Specifically, Naeem et al. used SIFT, GIST descriptors to encode the CT scan and reduced MAE to 0.190 [30] . Though this method analyzes global/local texture features, it adds an overhead for deriving these feature sets, also demands extensive hyperparameter fine-tuning.…”

“…Naeem et al. used SIFT, GIST features to build a CNN-LSTM fusion model for severity prediction [30] . Aboutalebi et al.…”

An End-to-End Lane Detection Model with Attention and Residual Block

“…Usually, the curve model of lane lines is constructed, and the lane line is approximately regarded as a straight-line model, a high-order curve model, and so forth. Recently, with the great success of deep learning in the field of computer vision [ 27 , 28 ], it is also widely used in the research of lane line detection, which brings new ideas for lane line detection [ 10 ]. More and more people apply deep learning to the task of lane line detection [ 15 ].…”