A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging
- 18 July 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Grid Computing
- Vol. 20 (3), 1-20
- https://doi.org/10.1007/s10723-022-09615-0
Abstract
The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.Keywords
This publication has 56 references indexed in Scilit:
- LSTM for diagnosis of neurodegenerative diseases using gait dataPublished by SPIE-Intl Soc Optical Eng ,2018
- A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classificationComputers in Biology and Medicine, 2018
- Predicting the Risk of Heart Failure With EHR Sequential Data ModelingIEEE Access, 2018
- Densely Connected Convolutional NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- WLD: A Robust Local Image DescriptorIEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
- Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image EnhancementJournal of Signal Processing Systems, 2004
- Evolving JPEG color data compression standardPublished by SPIE-Intl Soc Optical Eng ,1991
- Adaptive histogram equalization and its variationsComputer Vision, Graphics, and Image Processing, 1987
- Discrete Cosine TransformInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1974