Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Bhargava, Anuja; Bansal, Atul

doi:10.1007/s11042-021-10714-5

Cited by 51 publications

(27 citation statements)

References 94 publications

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“…This model architecture is applicable to many other eye related diseases, including longitudinal prognosis of glaucoma. Additionally, the model could be extended far beyond fundus photographs to aid in longitudinal evaluation of non-eye related diseases such as cancers 27 , COVID-19 28 , and other diseases and illnesses.…”

Section: Discussionmentioning

confidence: 99%

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

Ghahramani

Brendel

Lin

et al. 2021

Preprint

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Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) "deep-features" are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021.

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Section: Discussionmentioning

confidence: 99%

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

Ghahramani

Brendel

Lin

et al. 2021

Preprint

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“…Since the whole world is affected by coronavirus COVID-19 since December 2019 distinct studies have been started to diagnosis the disease. Last year, several studies have been done using deep learning, artificial learning, and neural network for automatic detection of COVID-19 [ 11 ]. In [ 36 ] utilizes a model of Efficient Net to detect COVID-19 using X-Ray images achieves 93.90%accuracy.…”

Section: Related Workmentioning

confidence: 99%

Machine learning-based automatic detection of novel coronavirus (COVID-19) disease

Bhargava

Bansal

Goyal

2022

Multimed Tools Appl

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The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics.

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“…So there's actually a lot of sense in using artificial and machine learning to help with the treatment of fungus [52]. If we talk about any image, then there are many steps in it to read the image like image application, segmentation, feature extraction, classification [18,21,22].…”

Section: Various Fungus Diagnosis By Computer Visionmentioning

confidence: 99%

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Nira

Kumar

2022

Multimed Tools Appl

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As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

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Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Cited by 51 publications

References 94 publications

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

Machine learning-based automatic detection of novel coronavirus (COVID-19) disease

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

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