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An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach
Author(s) -
Amin Javaria,
Anjum Muhammad Almas,
Sharif Muhammad,
Saba Tanzila,
Tariq Usman
Publication year - 2021
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23779
Subject(s) - artificial intelligence , pattern recognition (psychology) , convolutional neural network , computer science , deep learning , local binary patterns , feature selection , support vector machine , histogram of oriented gradients , discriminative model , histogram , segmentation , deep belief network , entropy (arrow of time) , feature extraction , image (mathematics) , physics , quantum mechanics
Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand‐crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U‐Net deep learning model. The hand‐crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand‐crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand‐crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand‐crafted features (ii) classification using fusion of hand‐crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand‐crafted & deep microscopic feature's fusion provide better results compared to only hand‐crafted fused features.