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

Abstract

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.

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

“…In studies conducted in 2021, Hussain et al 21 achieved 99.10% accuracy in binary classification but reached a 94.20% accuracy rate in three classifications in a similar dataset. Cengil and Çınar 11 detected COVID‐19 by combining the features obtained based on transfer learning in AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 models. The AlexNet + NASNetLarge and NASNetLarge + Xception approaches achieved 96.0% and 97.60% accuracy rates.…”

“… 9 , 10 For this reason, lung radiological chest scans are used in COVID‐19 case detection. 11 CT and chest x‐ray (CXR) are used for breast scanning. 12 Radiologists and physicians can distinguish positive COVID‐19 cases with the help of radiological lung imaging; this method is preferred for diagnosis as it gives both faster and more precise results when compared to the RT‐PCR method.…”

“…Recently, medical imaging data such as x‐rays, computed tomography (CT), and magnetic resonance imaging have been used to detect and diagnose many diseases such as brain tumors and lung 9,10 . For this reason, lung radiological chest scans are used in COVID‐19 case detection 11 . CT and chest x‐ray (CXR) are used for breast scanning 12 .…”

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

“…Shilby et al [43] came up with R-CNN, a deep learning architecture with 97.36% accuracy on 2-classes having 183 COVID-19 and 13619 (Normal+Pneumonia) images. Wang et al [46] proposed a weakly supervised framework for COVID-19 classification and lesion localization task and reported a 90% classification accuracy on a dataset with 313 COVID-19 images and 229 Healthy/Normal chest X-rays. Later, Ozturk et al [18] presented their model for binary and multiclass classification with 98.08% and 87.02% accuracy respectively on a dataset with 125, 500, 500 samples for COVID-19, Pneumonia and Normal classes respectively.…”

Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

“…All over the world, the COVID-19 virus remains a threat to the economies of countries and the health of people. It has been proven that the disease is transmitted from one person to another and, therefore, delays in discovering the disease lead to the spread of infection through interactions between the healthy and infected patients [ 2 , 3 , 4 ].…”