Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network

Abstract

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy.</p>

Enhancing the medical diagnosis of COVID-19 with learning based decision support systems

“…One common limitation is the small dataset size used for training. Several studies e.g., [8]- [12] were trained on a smaller dataset, which could lead to overfitting and poor generalization performance on new data. Additionally, performance may degrade rapidly due to the scarcity of data [13].…”

“…Therefore, it is important to have large and diverse datasets for deep learning models to learn robust and generalizable features. Another limitation is the lack of sensitivity analysis, as noted in several studies [8], [9], [13]. Sensitivity analysis helps understand how a model's performance changes with variations in the input data or model parameters.…”