Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images


Posted: 2021-09-11 19:00:00
The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application. Keywords: BGWO; CNN; Covid-19; DST; Ensemble; GWO; Hyperparameters; WOA.

参考サイト PubMed: covid-19


7月 22, 2020 bioassociates2

新型コロナウイルスがin vitroで心臓細胞に感染することが最近の研究で判明

米国のシーダーズ・サイナイ病院による新しい研究で、 COVID-19 を引き起こすSARS-CoV-2(新型コロナウイルス)が心臓細胞に感染する可能性があり、COVID-19患者の心臓細胞が直接感染する可能性があることが示された。 2020年6月25日にCell Reports Medicineのオンラインで公開されたこの発見は、iPS細胞技術によって生産された心筋細胞を使用して行われた。この論文は、「ヒトiPSC由来の心筋細胞はSARS-CoV-2感染の影響を受けやすい(Human iPSC-Derived…

ゲスト 594人 と メンバー 8人 がオンラインです