Classification of Coronavirus Images using Shrunken Features

Abstract

Necessary screenings must be performed to control the spread of the Corona Virus in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

“…Three phases are included in the proposed framework: feature extraction, feature selection, and classification. Firstly, with 411 samples, the classification results are examined in raw form for 88 features [ 42 ]. And then the ReliefF algorithm chooses the 10 most contributory characteristics [ 50 ].…”

“…Textural and spatial knowledge in the picture is collected in accordance with the LBGLCM process. The availability of the LBGLCM algorithm in image processing applications is improved by the simultaneous acquisition of this information [ 42 ].…”

“…e availability of the LBGLCM algorithm in image processing applications is improved by the simultaneous acquisition of this information [42].…”

“…Methodology Features Accuracy (%) Abdel-Nasser et al [2] Local database + GLCM + SVM 20 88.0% Pérez et al [18] DDSM + ROI + GLCM + GLCLM + Boolean vector 19 12 94.9% 92.3% Nababan et al [16] Mini MIAS + WFRFT + Jaya-FNN 18 92.27% Nababan [5] INbreast + ROI + GLCM + SECoS 16 82.98% Mohanty et al [49] DDSM + ROI + GLCM + GLCLM + decision tree 19 12 96.7% 93.6% Ozturk et al [42] Shrunken features + SMOTE + PCA 20 94. Journal of Healthcare Engineering mining techniques were considered.…”

Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

“…In this context, there are some studies conducted with lung imaging methods (X-ray or CT). Some of them are as follows: Studies with feature extraction and classification [4], [5], [6], studies performed with convolutional neural networks without external feature extraction, which are among the end-to-end methods [7], [8], [9] are studies with segmentation methods [10], [11].…”