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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.

Waleed Aldhahi¹, Sanghoon Sull¹

¹School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

Diagnostics (Basel, Switzerland)

|February 11, 2023

Summary

This study introduces a novel deep learning method for accurate COVID-19 detection from chest X-rays, achieving 99% accuracy. It enhances model explainability and reliability for critical medical applications.

Keywords:

COVID-19 deep learning explainable AI intelligent signal processing uncertainty

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Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Deep Learning for Diagnostics

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Distinguishing COVID-19 from other respiratory infections like pneumonia is challenging.
Explainability of deep learning models is crucial for clinical adoption.

Purpose of the Study:

To develop a highly accurate deep learning model for COVID-19 classification from chest X-rays.
To enhance the explainability and reliability of AI-driven medical diagnostics.
To address the cost and early detection challenges in pandemic management.

Main Methods:

Training deep learning models with an uncertainty-based ensemble voting policy.
Integrating cyclic cosine annealing, cross-validation, and uncertainty quantification (PICP) for model training.
Proposing the Uncertain-CAM technique for improved deep learning explainability.

Main Results:

Achieved 99% accuracy in classifying COVID-19 chest X-rays against normal and pneumonia cases.
Demonstrated enhanced explainability using the novel Uncertain-CAM technique.
Validated a new image processing method for explainability measurement.

Conclusions:

The proposed method offers a reliable and accurate AI solution for COVID-19 detection.
Enhanced explainability through Uncertain-CAM increases trust in AI diagnostic systems.
This approach can aid in cost-effective and early identification of COVID-19 patients.