COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

Sanagavarapu, Sowmya; Sridhar, S.; Gopal, T. Venu

doi:10.1109/iemtronics52119.2021.9422556

Cited by 14 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DenseNet and its variants were utilized in a number of deep learning articles, including [31,49,54,56,62,119]. ResNet and its variants were also widely utilized, with articles [31,39,44,46,47,49,50,54,55,58,59,[66][67][68]97] mentioning them. Researchers that have employed image processing and feature extraction for COVID-19 detection can be found in the image processing section.…”

Section: Discussionmentioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

View full text Add to dashboard Cite

COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with.

show abstract

Section: Discussionmentioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

View full text Add to dashboard Cite

show abstract

“…This section introduces and explains the specific techniques, algorithms, and data sources employed. The coefficient of determination 17 , also known as the goodness of fit, is expressed as R 2 . The coefficient of determination reflects what percentage of the fluctuations in the dependent variable y can be described by the fluctuations in x.…”

Section: Methodsmentioning

confidence: 99%

A data-driven short video international communication model based on indicator system communication network and attention BiLSTM neural network

Song,

Liu

2024

Sci Rep

View full text Add to dashboard Cite

As one of the most popular ways to disseminate Internet content in the current era of media convergence, short videos have increasingly become part of people’s daily lives. This paper takes TikTok as the research object, focusing on the study of short video communication process. The research objective of this paper is to improve the international communication effect of short videos through data-driven analysis and strategy research on the law of international communication of short videos. Firstly, the key objective indicator system about international communication of short videos is constructed, the definition, selection and design logic of key objective indicators are introduced, the construction idea of the corresponding indicator system is proposed, and then the key objective indicator system about international communication of short videos and related data set are constructed. Secondly, an international communication network model for short videos is built. The construction of this network model is divided into three steps, the construction of the short video communication network KOIS-PN based on the key objective indicators system, the construction of AT-BiLSTM neural network model to adapt to the research scenario of short video international communication, the construction of an international communication network model of short videos combining KOIS-PN and AT-BiLSTM. The international short video communication network is constructed according to the key objective index system of international short video, and the key objective index system is used as nodes in the communication network, and the short video communication network is constructed by hierarchical relationship. Thirdly, the performance of the core theoretical model proposed in this paper is verified. The experimental results show that, the KOISPN-ATBiLSTM international communication network model of short videos has certain validity and advancement.

show abstract

“…This prevents the over-amplification of noise that leads to the AHE model. CLAHE uses a contrast amplification limiting process that can be employed for every adjacent pixel later to form a transformation function for reducing the noise problem [18].…”

Section: Contrast Enhancement Processmentioning

confidence: 99%

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Yonbawi¹,

Alahmari²,

murthy³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops (MMTL-IPCAC) technique. The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization (CLAHE) approach for image enhancement. The neural architectural search network (NASNet) model is applied for feature extraction, and a modified grey wolf optimization (MGWO) algorithm is employed for the hyperparameter tuning process, showing the novelty of the work. At last, the extreme gradient boosting (XGBoost) model is utilized to carry out the insect classification procedure. The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

show abstract

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

Cited by 14 publications

References 38 publications

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

A data-driven short video international communication model based on indicator system communication network and attention BiLSTM neural network

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Contact Info

Product

Resources

About