Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification

2021

Cogn Comput

Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral groundglass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.

Section: Discussionsupporting

confidence: 85%

“…In the study conducted by Yang et al [47], LBP and CNN were combined in facial-expression recognition. Touahri et al [48] used these two methods in breast cancer diagnosis. In these studies, LBP was generally used as a pre-treatment; LBP-based CNN was introduced by Juefei-Xu et al [49].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

2021

Cogn Comput

“…In this context, a significant part of the best results obtained in the study was provided by using pipeline classification algorithm. In this sense, it is seen that the results of the study support some other literature studies [35]- [39] where CNN and LBP methods are used together and use of LBP was shown to increase the success of the relevant study.…”

Section: Discussionsupporting

confidence: 83%

A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

2020

Preprint

At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia develops due to Covid-19 virus in patients having severe disease symptoms. Many literature studies have been carried out in the process where the effects of the disease-induced pneumonia in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

“…In addition, using the original images and the images obtained by applying LBP process to these images provides an improvement in value of AUC and EER. These results measure up to supporting some other studies [27][28][29][30][31] where CNN and LBP methods were used together and LBP has been shown to increase study success. Within the scope of the study, the highest values of specificity and accuracy were obtained using CT images of lungs (gray-level) at values 0,9120 and 95,32%, respectively.…”

Section: Discussionsupporting

confidence: 83%

A New Radiomic Study on Lung CT Images of Patients with Covid-19 using LBP and Deep Learning (Convolutional Neural Networks (CNN))

2020

Preprint

The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, the amount of 25 lung CT images in total (15 of Covid-19 patients and 10 of normal) was multiplied (250 images in total) using three data augmentation methods which relate to contrast change, brightness change and noise addition, and these images were subjected to automatic classification. Within the scope of the study, experiments were made for each case which include the use of the CT images of lungs (gray-level and RGB) directly, the images obtained by applying Local Binary Pattern (LBP) to these images (gray-level and RGB) and the images obtained by combining these images (gray-level and RGB). In the study, a 23-layer Convolutional Neural Networks (CNN) architecture was developed and used in classification processes. Leave-one-group-out cross validation method was used to test the proposed system. In this context, the result of the study indicated that the best AUC and EER values were obtained for the combination of original (RGB) and LBP applied (RGB) images, and these figures are 0,9811 and 0,0445 respectively. It was observed that, applying LBP to images, the use of CNN input causes an increase in sensitivity values while it causes a decrease in values of specificity. The highest sensitivity was obtained for the case of using LBP-applied (RGB) images and has a value of 0,9947. Within the scope of the study, the highest values of specificity and accuracy were obtained by the help of CT of lungs (gray-level) with 0,9120 and 95,32%, respectively. The results of the study indicate that analyzing images of lung CT using deep learning methods in diagnosing Covid-19 disease will speed up the diagnosis and significantly reduce the burden on healthcare workers.