Effect of image transformation on EfficientNet model for COVID-19 CT image classification.

The Novel Corona Virus 2019 has drastically affected millions of people all around the world and was a huge threat to the human race since its evolution in 2019. Chest CT images are considered to be one of the indicative sources for diagnosis of COVID-19 by most of the researchers in the research community. Several researchers have proposed various models for the prediction of COVID-19 using CT images using Artificial Intelligence based algorithms (Alimadadi e al., 2020 [19], Srinivasa Rao and Vazquez, 2020 [20], Vaishya et al., 2020 [21]). EfficientNet is one of the powerful Convolutional Neural Network models proposed by Tan and Le (2019). The objective of this study is to explore the effect of image enhancement algorithms such as Laplace transform, Wavelet transforms, Adaptive gamma correction and Contrast limited adaptive histogram equalization (CLAHE) on Chest CT images for the classification of Covid-19 using the EfficientNet algorithm. SARS- COV-2 (Soares et al., 2020) dataset is used in this study. The images were preprocessed and brightness augmented. The EfficientNet algorithm is implemented and the performance is evaluated by adding the four image enhancement algorithms. The CLAHE based EfficientNet model yielded an accuracy of 94.56%, precision of 95%, recall of 91%, and F1 of 93%. This study shows that adding a CLAHE image enhancement to the EfficientNet model improves the performance of the powerful Convolutional Neural Network model in classifying the CT images for Covid-19.

Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.