Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 132, P. 104319 - 104319

Published: March 11, 2021

Language: Английский

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Transfer learning techniques for medical image analysis: A review

Journal of Applied Biomedicine, Journal Year: 2021, Volume and Issue: 42(1), P. 79 - 107

Published: Dec. 13, 2021

Language: Английский

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Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

Pattern Analysis and Applications, Journal Year: 2021, Volume and Issue: 24(3), P. 1111 - 1124

Published: March 19, 2021

COVID-19 continues to have catastrophic effects on the lives of human beings throughout world. To combat this disease it is necessary screen affected patients in a fast and inexpensive way. One most viable steps towards achieving goal through radiological examination, Chest X-Ray being easily available least expensive option. In paper, we proposed Deep Convolutional Neural Network-based solution which can detect +ve using chest images. Multiple state-of-the-art CNN models—DenseNet201, Resnet50V2 Inceptionv3, been adopted work. They trained individually make independent predictions. Then models are combined, new method weighted average ensembling technique, predict class value. test efficacy used publicly X-ray images COVID –ve cases. 538 468 divided into training, validation sets. The approach gave classification accuracy 91.62% higher than as well compared benchmark algorithm. We developed GUI-based application for public use. This be any computer by medical personnel within few seconds.

Language: Английский

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Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2021, Volume and Issue: 14(4), P. 3239 - 3259

Published: Sept. 18, 2021

Since the arrival of novel Covid-19, several types researches have been initiated for its accurate prediction across world. The earlier lung disease pneumonia is closely related to as patients died due high chest congestion (pneumonic condition). It challenging differentiate Covid-19 and diseases medical experts. X-ray imaging most reliable method prediction. In this paper, we propose a framework predictions like from images patients. consists dataset acquisition, image quality enhancement, adaptive region interest (ROI) estimation, features extraction, anticipation. used two publically available datasets. As degraded while taking X-ray, applied enhancement using median filtering followed by histogram equalization. For ROI extraction regions, designed modified growing technique that dynamic selection based on pixel intensity values morphological operations. detection diseases, robust set plays vital role. We extracted visual, shape, texture, each normalization. normalization, formulated enhance classification results. Soft computing methods such artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, deep learning classifier are classification. disease, architecture has proposed recurrent (RNN) with long short-term memory (LSTM). Experimental results show robustness efficiency model in comparison existing state-of-the-art methods.

Language: Английский

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Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images

Multimedia Systems, Journal Year: 2021, Volume and Issue: 28(4), P. 1401 - 1415

Published: July 6, 2021

Language: Английский

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Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 142, P. 105213 - 105213

Published: Jan. 5, 2022

Language: Английский

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Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 55(3), P. 3551 - 3603

Published: Sept. 16, 2022

Language: Английский

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ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier

Al-Salam Journal for Engineering and Technology, Journal Year: 2023, Volume and Issue: 2(2), P. 116 - 127

Published: April 13, 2023

ChatGPT is a state-of-the-art language model developed by OpenAI. It part of the GPT (Generative Pre-trained Transformer) series, which are designed to generate human-like output based on large amounts input data. one largest and most advanced models date, with 175 billion parameters. The article aims examine impact artificial intelligence tools techniques academic research their potential implications for ethics. In particular, this will focus practices in generating scientific within context powerful tool that can text different formats, conduct literature searches, suggest titles created text. However, using select topics low similarity score checkers may lead ethical violations. This finds use applications raise concerns about ethics, limited availability technologies detect such violations poses significant challenge writing.

Language: Английский

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A systematic literature review of machine learning application in COVID-19 medical image classification

Procedia Computer Science, Journal Year: 2023, Volume and Issue: 216, P. 749 - 756

Published: Jan. 1, 2023

Detecting COVID-19 as early possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help diagnose more accurately. This report aims find out how far research has progressed what lessons be learned for future in this sector. By filtering titles, abstracts, content Google Scholar database, literature review was able 19 related papers answer two questions, i.e. medical images are commonly used classification methods classification. According findings, chest X-ray were most data categorize transfer techniques method study. Researchers also concluded that lung segmentation use multimodal could improve performance.

Language: Английский

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Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 26, 2024

Language: Английский

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