matlab calibration results Search Results


2019b  (MathWorks Inc)
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nlmefit function  (MathWorks Inc)
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matlab/simulink pog model  (MathWorks Inc)
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calibration results  (MathWorks Inc)
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MathWorks Inc calibration results
Calculation process of the A/N ratio using <t>MATLAB</t> and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
Calibration Results, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab toolbox  (MathWorks Inc)
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MathWorks Inc matlab toolbox
Calculation process of the A/N ratio using <t>MATLAB</t> and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
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curve fitting toolbox  (MathWorks Inc)
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Calculation process of the A/N ratio using <t>MATLAB</t> and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.
Curve Fitting Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.

Journal: Nature and Science of Sleep

Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

doi: 10.2147/NSS.S492146

Figure Lengend Snippet: Calculation process of the A/N ratio using MATLAB and deep learning model architecture. ( A ) A fiberoptic nasopharyngoscopy image was selected, highlighting the adenoid cross-sectional area and the nasopharynx. The developed algorithm was then applied to automatically calculate the A/N ratio. ( B ) The network architecture used for both training and testing stages consisted of identical components, including essential modules such as Backbone, Neck, Decoder Head, and Loss, along with optional modules like the Neck and Auxiliary Head.

Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

Techniques:

Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.

Journal: Nature and Science of Sleep

Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

doi: 10.2147/NSS.S492146

Figure Lengend Snippet: Scatterplot of the Mann–Whitney U -test for A/N ratio evaluations. ( A ) Each scatter point represents an individual A/N ratio value assessed by the two experts. ( B ) Each scatter point corresponds to an individual A/N ratio value calculated by the MATLAB algorithm, based on calibration performed by the same two experts. This visual representation offers a comprehensive comparison between the expert evaluations and the MATLAB-calculated A/N ratio values.

Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

Techniques: MANN-WHITNEY, Comparison

Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).

Journal: Nature and Science of Sleep

Article Title: Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

doi: 10.2147/NSS.S492146

Figure Lengend Snippet: Confusion matrix of adenoid hypertrophy degree performance for deep learning method and MATLAB algorithm. Adenoid hypertrophy degree is classified into three categories: small (A/N ratio 0–50%), medium (A/N ratio 50–75%), and large (A/N ratio 75–100%). In each confusion matrix, the horizontal axis represents the MATLAB results (actual class), while the vertical axis represents the deep learning results (predicted class).

Article Snippet: Additionally, MATLAB calibration results demonstrated a p value of 0.679, indicating no significant differences between their outcomes.

Techniques: