RETRACTED ARTICLE: Application of convolutional neural network-based biosensor and electroencephalogram signal in sleep staging

Zhou, Yi; Zhao, Dezhu

doi:10.1007/s12652-021-03076-1

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…Data augmentation techniques may include flipping, rotating, and shifting the images to increase the size of the dataset and improve the model's robustness. [3] Split Data: Split the dataset into training, validation, and testing sets. [4] Model Architecture: Use the U-Net architecture, which is a deep learning model used for image segmentation.…”

Section: Methodsmentioning

confidence: 99%

Diabetic Foot Ulcer Identification Using Unet Variants of Transfer Learning

2023

IRJMETS

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Diabetic foot ulcers (DFUs) are a major complication of diabetes, affecting up to 25% of diabetic patients over their lifetime. These ulcers are often slow to heal, can lead to serious infections, and may even require amputation in severe cases. Early identification and treatment of DFUs is crucial to prevent complications and improve patient outcomes. In recent years, advances in medical imaging technology and machine learning algorithms have made it possible to accurately identify DFUs using computer vision techniques. One such technique is the use of the U-Net architecture, a deep learning algorithm that is well-suited for medical image segmentation. U-Net algorithm is a type of convolutional neural network (CNN) that is designed to perform pixel-level segmentation of images. It consists of an encoder network that extracts features from the input image and a decoder network that reconstructs the output segmentation map. The U-Net architecture has been used successfully in various medical imaging applications, including identifying skin lesions, breast tumors, and lung nodules. To apply the U-Net algorithm to DFU identification, a dataset of medical images is needed. This dataset should include both normal and ulcerated foot images, and should be labeled to indicate which pixels correspond to the ulcerated area. Once the dataset has been created, the U-Net algorithm can be trained using a supervised learning approach, where the algorithm learns to identify DFUs by comparing its segmentation predictions to the ground truth labels. One advantage of using the U-Net algorithm for DFU identification is its ability to handle images of different sizes and resolutions. This is important because medical images can vary widely in their quality and dimensions, depending on the imaging technique used.

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Section: Methodsmentioning

confidence: 99%

Diabetic Foot Ulcer Identification Using Unet Variants of Transfer Learning

2023

IRJMETS

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“…In addition, the notion of a smart healthcare platform was developed, focusing on activities linked to enabling the development of mobile health applications, such as arranging user data access, management, and sharing. The technical foundations of machine learning, big data analysis, cloud computing, and IoT in clinical medicine were investigated by Zhao-xia Lu et al [40].…”

Section: Review: Iot or Iomt Including Covid-19mentioning

confidence: 99%

“…Electroencephalogram (EEG) sensor [98], pulse oximeter [99], electromyography (EMG) sensor [100], etc., help in the collecting real-time medical health parameters. The data of locations where COVID-19 symptoms are detected is generated by GPS sensors [40] with thermal imaging technology [101]. IoMT sensors are also used in cov-AID for tracking the origin of the outbreak and help people to identify COVID-19 positive patients, ask them to maintain saved distance, and complete the quarantine period.…”

Section: Big Data Generation Layermentioning

confidence: 99%

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

et al. 2022

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The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.

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