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


6月 06, 2021 バイオアソシエイツ


COVID-19 パンデミックが始まってから数カ月後の2020年初頭、科学者らはCOVID-19感染症の原因ウイルスであるSARS-CoV-2の全ゲノム配列を決定することができた。その時点で、その遺伝子の多くはすでに判明していたが、タンパク質をコードする遺伝子の全容は解明されていなかった。今回、MITの研究者らが広範な比較ゲノム研究を行った結果、SARS-CoV-2のゲノムについて、最も正確で完全な遺伝子アノテーションを作成した。 この研究結果は、2021年5月11日にNature…