A Reread Deep Neural Network for Multiclass Lung Disease Detection and Classification
Keywords:
COVID-19, Reread Deep Neural Network, hand-crafted features, deep features, Computed Tomography ImagesAbstract
COVID-19 is a dangerous and extremely infectious virus, destroyed the life of million people throughout the world. Early viral identification can lower virus propagation and mortality rates. A number of different detection techniques have been implemented to identify COVID-19 in Computed Tomography (CT) scans. Amongst, Chaotic Logistic Map based Modified Whale Optimization with Improved Neural Network (CLM-MWO-INN) provides an efficient result in COVID-19 prediction. However, it cannot process large size samples. Hence, in this paper, a Reread Deep Neural Network (RDNN) model is proposed for processing the large number of samples from CT images. Gray Level Run Length Matrix (GLRLM) and Gray Level Co-Occurrence Matrix (GLCM) is used to produce the handmade features, whereas RDNN's convolutional layers are used to extract the deep features. These extracted attributes are combined and appropriate features are selected by CLM-MWO. The CLM-MWO is also used for selecting an appropriate parameter of RDNN.The feature and parameter selection are simultaneously performed in this model, because the selected features and parameter might affect the RDNN performance. The selected features by CLM-MWO are fed into the softmax layer classifies CT lung scan images into multiple classes, including Atelectasis, Pneumonia, Infiltrate, COVID-19, and Non-Diseased. The suggested approach yields favorable outcomes when compared to traditional DNN architectures, exhibiting improved convergence speed with larger datasets. The complete work is named as CLM-MWO with RDNN (CLM-MWO-RDNN). At last, the results show that compared to state-of-the-art models, the CLM-MWO-RDNN model outperforms with an accuracy of 98.59% (single validation) and 97.28% (4-fold cross validation) on the SARS-CoV-2 CT scan dataset, 97.74% (without augmentation) and 96.32% (with augmentation and 4-fold cross validation) on Customized Lung disease dataset than ViT- LSTM and ViT-CNN-LSTM models.
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