Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID‐19 classification

• Published in: Concurrency and computation Vol. 34; no. 15; pp. e6958 - n/a
• Main Authors: Akbar, Sumaiya Begum, Thanupillai, Kalaiselvi, Sundararaj, Suganthi
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.07.2022; Wiley Subscription Services, Inc
• Subjects: artificial intelligence; Artificial neural networks; Classification; Classifiers; computed tomography; COVID-19; Datasets; Equalization; Feature extraction; Histograms; Image enhancement; medical image and accuracy; Neural networks; Search algorithms; Stacking; computed tomography; COVID‐19; medical image and accuracy; artificial intelligence
• ISSN: 1532-0626; 1532-0634; 1532-0634
• DOI: 10.1002/cpe.6958

In this article, COVID‐19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID‐19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images. The FGLHE technique and fuzzy stacking technique are combined together and forms into stacked dataset image. This stacked dataset are trained with AlexNet convolutional deep neural network model and the feature packages acquired via the models are processed by the anopheles search algorithm. Subsequently, the efficient features are combined and delivered to random forest (RF) classifier. The proposed approach is implemented in MATLAB. The proposed ADCNN‐ASA‐RFC provides 91.66%, 69.13%, 34.86%, and 70.13% higher accuracy, 79.13%, 60.33%, and 63.34% higher specificity and 77.13%, 58.45%, 25.86%, and 55.33%, higher sensitivity compared with existing algorithms. At last, the simulation outcomes demonstrate that the proposed system can be able to find the optimal solutions efficiently and accurately with COVID‐19 diagnosis.