Automated sleep stage classification in sleep apnoea using convolutional neural networks

Sundar, G. Naveen; Narmadha, D.; Jone, A. Amir Anton; Sagayam, K. Martin; Dang, Hien; Pomplun, Marc

doi:10.1016/j.imu.2021.100724

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…The 180 • and 360 • iris row values were computed using Equation (18). The iris centre row and column values were computed using Equations (19) and (20). The iris diameter and radius were calculated using Equations ( 21) and (22).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The authors also developed a multiple left-right point (MLRP) algorithm for detecting pixels in the iris boundary using the visible iris region [18]. There have been many studies applying deep learning, such as CNN to classification for disease diagnosis [19][20][21]. Matten et al [22] proposed diabetic retinopathy (DR) detection using a CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

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An effective model for the iris regional characteristics and classification using deep learning alex network

Thiyaneswaran¹,

Sengottaiyan²,

Kulandairaj

et al. 2022

IET Image Processing

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Iris biometrics is one of the fastest-growing technologies, and it has received a lot of attention from the community. Iris-biometric-based human recognition does not require contact with the human body. Iris is a combination of crypts, wolflin nodules, concentrated furrows, and pigment spots. The existing methods feed the eye image into deep learning network which result in improper iris features and certainly reduce the accuracy. This research study proposes a model to feed preprocessed accurate iris boundary into Alexnet deep learning neural network-based system for classification. The pupil centre and boundary are initially recorded and identified from the given eye images. The iris boundary and the centre are then compared for the identification using the reference pupil centre and boundary. The iris portion, exclusive feature of the pupil area is segmented using the parameters of multiple left-right point (MLRP) algorithms. The Alexnet deep learning multilayer networks 23, 24, and 25 are replaced according to the segmented iris classes. The remaining Alexnet layers are trained using the square gradient decay factor (GDF) in accordance with the iris features. The trained Alexnet iris is validated using suitable classes. The proposed system classifies the iris with an accuracy of 99.1%. The sensitivity, specificity, and F1-score of the proposed system are 99.68%, 98.36%, and 0.995. The experimental results show that the proposed model has advantages over current models.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An effective model for the iris regional characteristics and classification using deep learning alex network

Thiyaneswaran¹,

Sengottaiyan²,

Kulandairaj

et al. 2022

IET Image Processing

Self Cite

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show abstract

“…The attention mechanism is paired with a spatial pyramid to extract exact dense features for pixel labelling, which is different from previously proposed segmentation architectures. Owing to the introduction of a pyramidal attention block 32 , 33 on the bridge, this network can segment nodules of different sizes using high-performance metrics.…”

Section: Methodsmentioning

confidence: 99%

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Bruntha

Pandian

Sagayam

et al. 2022

Sci Rep

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Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%.

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“…shows some of the major advantages of e-healthcare. Research on healthcare big data is exceptionally captious in improving the accuracy of diagnoses and developing ingenious medicines [5]- [7]. Generally, healthcare organizations share health records with research organizations for medical discoveries [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

et al. 2022

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Advancements in Industry 4.0 brought tremendous improvements in the healthcare sector, such as better quality of treatment, enhanced communication, remote monitoring, and reduced cost. Sharing healthcare data with healthcare providers is crucial for harnessing the benefits of such improvements. In general, healthcare data holds sensitive information about individuals. Hence, sharing such data is challenging because of various security and privacy issues. According to privacy regulations and ethical requirements, it is essential to preserve the privacy of patients before sharing data for medical research. State-of-the-art literature on privacy preserving studies either uses cryptographic approaches to protect the privacy or uses anonymizing techniques regardless of the type of attributes, this results in poor protection and data utility. In this paper, we propose an attribute-focused privacy preserving data publishing scheme. The proposed scheme is two-fold, comprising a fixed-interval approach to protect numerical attributes and an improved l-diverse slicing approach to protect the categorical and sensitive attributes. In the fixed-interval approach, the original values of the healthcare data are replaced with an equivalent computed value. The improved l-diverse slicing approach partitions the data both horizontally and vertically to avoid privacy leaks. Extensive experiments with real-world datasets are conducted to evaluate the performance of the proposed scheme. The classification models built on anonymized dataset yields approximately 13% better accuracy than benchmarked algorithms. Experimental analyses show that the average information loss which is measured by normalized certainty penalty (NCP) is reduced by 12% compared to similar approaches. The attribute focused scheme not only provides data utility but also prevents the data from membership disclosures, attribute disclosures, and identity disclosures.

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Automated sleep stage classification in sleep apnoea using convolutional neural networks

Cited by 9 publications

References 10 publications

An effective model for the iris regional characteristics and classification using deep learning alex network

An effective model for the iris regional characteristics and classification using deep learning alex network

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

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