Classification of COVID-19 cases using Fine-Tune Convolution Neural Network (FT-CNN)

Degadwala, Sheshang; Vyas, Dhairya

doi:10.1109/icais50930.2021.9395864

Cited by 21 publications

(4 citation statements)

References 17 publications

(10 reference statements)

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“…It is noticed that the first term and second term are similar to 𝐶𝑜𝑛𝑣 function in terms of characteristics. Therefore, let 𝒲 𝑓𝑢𝑠𝑒𝑑 and ℬ 𝑓𝑢𝑠𝑒𝑑 be the first and second terms, the fusion function can be rewritten in (8). Besides, 1×1 Conv is able to be replaced by 3×3 Conv by zero-padding and adjusting the center value of kernel.…”

Section: U-net With Variational Data Imputationmentioning

confidence: 99%

“…Despite the amelioration in sensitivity, the classification accuracy is 91.9% which is less than the COVID-Net. Transfer learning from pre-trained models is also applied in the work [8]- [10]. By finetuning, the network is able to learn the new features based on the CXR images and this process also gives a better result compared with training from scratch in terms of accuracy and training time.…”

Section: Introductionmentioning

confidence: 99%

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Improving RepVGG model with variational data imputation in COVID-19 classification

Trang

Nguyen

TonThat

et al. 2022

IJ-AI

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<span>Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.</span>

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Section: U-net With Variational Data Imputationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving RepVGG model with variational data imputation in COVID-19 classification

Trang

Nguyen

TonThat

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Several authors also applied ML models to covid-19. Degadwala, S [3]. et al applied Convolutional Neural Networks for the classification of covid-19 cases.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

2021

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<p>The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language</p>

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“…In this article, we present a detailed study and comparison of conventional optimization and ML-assisted optimization. Although ML models have been extensively used in fields such as biometrics [13][14][15], healthcare [16][17][18], and finance [19,20], their application in electromagnetics and antenna design optimization is relatively new [21]. Previous studies have utilized various ML models to optimize antenna designs, as summarized in [22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

Indharapu,

Caruso,

Fields

et al. 2024

PIER C

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In the wireless communication industry, achieving gigabit-per-second data rates with low-profile, ultra-wideband (UWB) microstrip patch antennae poses a significant challenge. Conventional optimization algorithms, though effective, are often computationally expensive, particularly for complex antenna geometries with high degrees of freedom. There is an imperative need for new methodologies to address this challenge and revolutionize the antenna optimization process. Successful and timely development of antennas relies on the efficiency and computational speed of optimization algorithms, full-wave electromagnetic solvers, and the intuition of radio frequency engineers. To mitigate the dependence on complex and time-consuming processes, we propose an efficient machine learning (ML)-based antenna optimization methodology that minimizes optimization time by more than 90%. This paper aims to apply and study the performance of two specific ML models, the radial basis function (RBF), and the least squared regression (LSR) models, in the bandwidth optimization without increasing the aperture area of a hexagon-shaped fractal antenna. The hexagon-shaped fractal antenna was chosen for its UWB characteristics, low profile, and high degrees of freedom (10 adjustable parameters). The reflection coefficient response of a hexagon-shaped fractal antenna is predicted by the trained RBF and LSR models and further optimized by the genetic algorithm (GA). The proposed approach stands out among other notable works in this research domain, especially for UWB applications, by prioritizing the optimization of the mean of the reflection coefficient across the entire frequency range instead of solely targeting individual frequency points. The GA-based optimization using trained ML models has increased the bandwidth by 21.3% and reduced the computational time by 90% compared to conventional optimization without increasing the physical or electrical size of the antenna. Simulation and measurement results concurred with a maximum difference of 5%, demonstrating the efficacy of the ML approach for antenna optimization.

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Classification of COVID-19 cases using Fine-Tune Convolution Neural Network (FT-CNN)

Cited by 21 publications

References 17 publications

Improving RepVGG model with variational data imputation in COVID-19 classification

Improving RepVGG model with variational data imputation in COVID-19 classification

An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

Machine Learning-based Optimization of Hexagon-shaped Fractal Antenna for Ultra-wideband Communications

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