5 Must-Read Research in Image Processing Against COVID 19.
By: Rutuja Pawar
Tags: Image Processing, Covid 19, Technology, Research

5 Must-Read Research in Image Processing Against COVID 19.

W hile staying up-to-date with the latest trend and research its important to consider the technology used in the latest work and hence here are some informative extract from the top 5 must-know research to fight COVID-19 in classification, detection and testing methods performed using image processing to add value to your knowledge. Rutuja Pawar Dec 22, 2020·5 min read

Today, the entire world is facing productivity loss due to life-threatening pandemic, but the effective development in technology along with the advance and efficient medical research has been a boon to overcome this tough time.

1. Covid-19 test based on fluorescence and image analysis in 5-minute:

Recently, The Oxfords University developed a method which could distinguish between virus type, providing faster and more accurate result than PCR. The process involves the collection of fluorescently-labelled viruses image sample using a microscope. Later Machine-Learning software automatically detects the virus rapidly. Various virus type counting on their short fluorescent DNA strands in this approach includes differentiable labels like surface chemistry, size and shape. Validating the assay on Covid-19 patient sample are confirmed by conventional RT-PCR (real-time polymerase chain reaction) methods.

The research is based on deep learning along with single-particle imaging and uses CNN technology to categorise a variety of intacted particle of the virus from provided microscopic images. The process is done within 5 minutes without any amplification, purification and lysis steps. Once labelling, imaging and identification of virus are done, the trained neural network SARS-CoV-2 was detected from a variety of respiratory pathogens and negative clinical samples, providing high accuracy.

2. Image-based diagnosis of COVID-19 using a new machine learning method:

With 96.09% and 98.09% of accuracy rate, This research uses new Fractional Multichannel Exponent Moments (FrMEMs) on chest X-ray classification. The computational process is boosted using parallel multi-core computational framework.

Image classification is done using image moments which is utilized in feature extraction of 961 features in an input image, this is robust to noise, could be used in real-time providing effective computation in the inexpensive and faster way using a parallel implementation. Modified Manta-Ray Foraging Optimization (MRFO) based on Differential Evolution (DE) used as a feature selection method. The future model is trained and tested to compute the fitness function.

3. Deep features and fractional-order marine predators algorithm for COVID-19 image classification:

The computation cost of CNN could sometimes counter its effect even though being the state of the art, hence in this research, the CNNs are used with Inception(pre-trained model)approach used being hybrid classification along with swarm-based feature selection algorithm, referred to as Marine Predator algorithm helping within the selection of extracting required feature from COVID images.

This uses fractional-order calculus(FO) along with its mathematic tool marine predators algorithm (FO-MPA), output providing a reduction of computational complexity and increasing performance efficiency of computation. The model successfully selects 130,86 features from two datasets by international Cardiothoracic radiologist, researchers consisting of 51K features with classification accuracy around 98%, outperforming several CNNs. The model implemented in Google Colabs and developed using Keras library.

4. Transfer Learning Using Multimodal Imaging Data for COVID-19 Detection :

The research selects an optimal model after comparing with different CNN model i.e. VGG19 pre-trained model describing the model’s highly scarce and challenging dataset. the challenge faced is due to availability of large dataset in good quality and its impact on the trainability of the model. For development and testing DL model, an image preprocessing is proposed creating quality datasets. This highlights the technique of ultrasonic image over CT scan or X-ray images.

This has drawbacks which emphasize on the deeper network and after training, further less consistent behaviour is seen after three imaging modes. The pre-trained models provide a considerate level of COVID-19 detection with up to 100%for ultrasound, 86% for x-ray 84% for CT scans.

5. Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model for COVID-19 Detection :

This research focuses on the X-ray and CT scan images of the chest of the covid suspect using CNN based multi-image augmentation technique, which using discontinuity information obtained in the images in order to increase the count of the effectiveness of training the model.

The model when compared with ResNet-50 and VGG-16 pre-trained models achieves an accuracy of around 95.38% and 98.97% for chest CT scan and X-ray respectively. (these are preliminary reports that haven’t been peer-reviewed.)

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Authors:Rutuja Pawar, Ritu Pande, Uddhav Sabale, Varad Kulkarni, Kedar Dhokarikar.

REFERENCES:

Paper 1:Nicolas Shiaelis, Alexander Tometzki, View ORCID ProfileLeon Peto, Andrew McMahon, Christof Hepp, View ORCID ProfileErica Bickerton, View ORCID ProfileCyril Favard, View ORCID ProfileDelphine Muriaux, View ORCID ProfileMonique Andersson, View ORCID ProfileSarah Oakley, Alison Vaughan, View ORCID ProfilePhilippa C. Matthews, View ORCID ProfileNicole Stoesser, View ORCID ProfileDerrick Crook, View ORCID ProfileAchillefs N. Kapanidis, View ORCID ProfileNicole C. Robb doi: https://doi.org/10.1101/2020.10.13.20212035 https://www.medrxiv.org/content/10.1101/2020.10.13.20212035v3

Paper 2:Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT (2020) New machine learning method for image-based diagnosis of COVID-19. PLoS ONE 15(6): e0235187. doi:10.1371/journal.pone.0235187 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235187

Paper 3:Sahlol, A.T., Yousri, D., Ewees, A.A. et al. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Sci Rep 10, 15364 (2020).https://doi.org/10.1038/s41598-020-71294-2 https://www.nature.com/articles/s41598-020-71294-2/figures/1

Paper 4: M. J. Horry et al., “COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data,” in IEEE Access, vol. 8, pp. 149808–149824, 2020, doi: 10.1109/ACCESS.2020.3016780. https://ieeexplore.ieee.org/document/9167243

Paper 5: Kiran Purohit, Abhishek Kesarwani, Dakshina Ranjan Kisku, Mamata Dalui doi:https://doi.org/10.1101/2020.07.15.205567 https://www.biorxiv.org/content/10.1101/2020.07.15.205567v1.full