A Review of Deep Learning Algorithms and Their Applications in Healthcare

Algorithms, Год журнала: 2022, Номер 15(2), С. 71 - 71

Опубликована: Фев. 21, 2022

Deep learning uses artificial neural networks to recognize patterns and learn from them make decisions. is a type of machine that mimic the human brain. It methods such as supervised, semi-supervised, or unsupervised strategies automatically in deep architectures has gained much popularity due its superior ability huge amounts data. was found approaches can be used for big data analysis successfully. Applications include virtual assistants Alexa Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, colorization black white images, addition sound silent films, pixel restoration, dreaming. As review, this paper aims categorically cover several widely algorithms along with their practical applications: backpropagation, autoencoders, variational restricted Boltzmann machines, belief networks, convolutional recurrent generative adversarial capsnets, transformer, embeddings models, bidirectional encoder representations transformers, attention processing. In addition, challenges are also presented paper, AutoML-Zero, architecture search, evolutionary learning, others. The pros cons these applications healthcare explored, alongside future direction domain. This presents review checkpoint systemize popular encourage further innovation regarding applications. For new researchers field help obtain many details about advantages, disadvantages, applications, working mechanisms number algorithms. we introduce detailed information on how apply healthcare, relation COVID-19 pandemic. By presenting one section, hope increase awareness challenges, they dealt with. could motivate find solutions challenges.

Язык: Английский

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Initial Stage COVID-19 Detection System Based on Patients’ Symptoms and Chest X-Ray Images

Applied Artificial Intelligence, Год журнала: 2022, Номер 36(1)

Опубликована: Апрель 18, 2022

The accurate diagnosis of the initial stage COVID-19 is necessary for minimizing its spreading rate. physicians most often recommend RT-PCR tests; this invasive, time-consuming, and ineffective in reducing spread rate COVID-19. However, can be minimized by using noninvasive fast machine learning methods trained either on labeled patients' symptoms or medical images. cannot differentiate between different types pneumonias like COVID-19, viral pneumonia, bacterial pneumonia because similar symptoms, i.e., cough, fever, headache, sore throat, shortness breath. images have potential to overcome limitation symptom-based method; however, these are incapable detecting infection takes 3 12 days appear. This research proposes a detection system with detect employing deep models over chest X-Ray proposed obtained average accuracy 78.88%, specificity 94%, sensitivity 77% testing dataset containing 800 better than existing methods.

Язык: Английский

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FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 89, С. 105893 - 105893

Опубликована: Янв. 3, 2024

Язык: Английский

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Role of Artificial Intelligence in COVID-19 Detection

Sensors, Год журнала: 2021, Номер 21(23), С. 8045 - 8045

Опубликована: Дек. 1, 2021

The global pandemic of coronavirus disease (COVID-19) has caused millions deaths and affected the livelihood many more people. Early rapid detection COVID-19 is a challenging task for medical community, but it also crucial in stopping spread SARS-CoV-2 virus. Prior substantiation artificial intelligence (AI) various fields science encouraged researchers to further address this problem. Various imaging modalities including X-ray, computed tomography (CT) ultrasound (US) using AI techniques have greatly helped curb outbreak by assisting with early diagnosis. We carried out systematic review on state-of-the-art applied CT, US images detect COVID-19. In paper, we discuss approaches used authors significance these research efforts, potential challenges, future trends related implementation an system during pandemic.

Язык: Английский

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Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Healthcare, Год журнала: 2022, Номер 10(1), С. 85 - 85

Опубликована: Янв. 2, 2022

Machine Learning methods can play a key role in predicting the spread of respiratory infection with help predictive analytics. techniques mine data to better estimate and predict COVID-19 status. A Fine-tuned Ensemble Classification approach for death cure rates patients from using has been proposed different states India. The classification model is applied recent dataset India, performance evaluation various state-of-the-art classifiers performed. forecasted patients' status regions plan resources response care systems. appropriate output class based on extracted input features essential achieve accurate results classifiers. experimental outcome exhibits that Hybrid Model reached maximum F1-score 94% compared Ensembles other like Support Vector Machine, Decision Trees, Gaussian Naïve Bayes 5004 instances through 10-fold cross-validation right class. feasibility automated prediction Indian was demonstrated.

Язык: Английский

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Augmenting Sensor Performance with Machine Learning Towards Smart Wearable Sensing Electronic Systems

Advanced Intelligent Systems, Год журнала: 2022, Номер 4(4)

Опубликована: Янв. 19, 2022

Wearable sensing electronic systems (WSES) are becoming a fundamental platform to construct smart and intelligent networks for broad applications. Various physiological data readily collected by the WSES, including biochemical, biopotential, biophysical signals from human bodies. However, understanding these data, such as feature extractions, recognitions, classifications, is largely restrained because of insufficient capacity when using conventional processing techniques. Recent advances in performance system‐level operation quality WSES expedited with assistance machine learning (ML) algorithms. Here, state‐of‐the‐art ML‐assisted summarized emphasis on how accurate perceptions under different algorithms paradigm augment diverse Concretely, ML that frequently implemented studies first synopsized. Then applications strengthened functions discussed following sections, monitoring, disease diagnosis, on‐demand treatments, assistive devices, human–machine interface, multiple sensations‐based virtual augmented reality. Finally, challenges confronted addressed.

Язык: Английский

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A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture

Опубликована: Июнь 21, 2024

Today, we are in a new era of information and data where companies know what need according to our behavior within the network, they can understand predict products offers that want be shown market by using people's especially social networking sites create marketing campaigns for their products, which will gain, expectations acceptance great desire spark from followers reduce possible loss products. This is provided through machine learning techniques science. They work provide large enormous quantities sufficient time. All this collected certainly establish dramatic give patterns high probability continuation generating predictions future how benefit it make decisions. chapter review three noteworthy topics: learning, deep Also, significance artificial intelligence agricultural revolution contributes growth sector remarked. also adds about its now future.

Язык: Английский

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Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images

International Journal of Environmental Research and Public Health, Год журнала: 2023, Номер 20(2), С. 1268 - 1268

Опубликована: Янв. 10, 2023

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants emerging. Therefore, to prevent virus from spreading, must be diagnosed soon possible. COVID-19 has had a devastating impact on people’s health and economy worldwide. For detection, reverse transcription-polymerase chain reaction testing benchmark. However, this test takes long time necessitates lot laboratory resources. A trend emerging address these limitations regarding use machine learning deep techniques for automatic analysis, can attain high diagnosis results, especially by using medical imaging techniques. key question arises whether chest computed tomography scan or X-ray used detection. total 17,599 images were examined in work develop models classify occurrence infection, while four different classifiers studied. These are convolutional neural network (proposed architecture (named, SCovNet) Resnet18), support vector machine, logistic regression. Out all models, proposed SCoVNet reached best performance an accuracy almost 99% 98% images, respectively.

Язык: Английский

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Scientometric analysis of ICT-assisted intelligent control systems response to COVID-19 pandemic

Neural Computing and Applications, Год журнала: 2023, Номер 35(26), С. 18829 - 18849

Опубликована: Июнь 27, 2023

Язык: Английский

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Accurate detection of COVID-19 using deep features based on X-Ray images and feature selection methods

Computers in Biology and Medicine, Год журнала: 2021, Номер 137, С. 104771 - 104771

Опубликована: Авг. 19, 2021

Язык: Английский

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