Deep_Saliency: A Deep Learning based Saliency Approach to detect Covid-19 through x-ray images

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

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“…Verma et al 21 performed predictive analysis to verify the COVID‐19 positive case using the long short‐term memory (LSTM). In Ref.…”

Section: Introductionmentioning

confidence: 99%

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

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2021

Int J Imaging Syst Tech

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Deep_Saliency: A Deep Learning based Saliency Approach to detect Covid-19 through x-ray images

Cited by 1 publication

References 31 publications

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

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