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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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Related Experiment Video

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Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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A benchmark for Rey-Osterrieth complex figure test automatic scoring.

Juan Guerrero-Martín1, María Del Carmen Díaz-Mardomingo2, Sara García-Herranz3

  • 1Department of Artificial Intelligence, UNED, Madrid, Spain.

Heliyon
|November 18, 2024
PubMed
Summary

This study introduces a new benchmark for automatically scoring the Rey-Osterrieth complex figure (ROCF) test, aiding early cognitive decline detection. A sketch-optimized CNN achieved strong results, establishing a baseline for future research.

Keywords:
BenchmarkCognitive impairment detectionDeep learningRey-Osterrieth complex figure scoringTransfer learning

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Machine Learning

Background:

  • The Rey-Osterrieth complex figure (ROCF) test is crucial for detecting cognitive decline in older adults.
  • Automating ROCF test analysis with computer vision is challenging due to a lack of standardized benchmarks.
  • Existing automated systems lack a fair comparison framework, hindering progress.

Purpose of the Study:

  • To establish a benchmarking framework for the automatic scoring of the ROCF test.
  • To introduce the ROCFD528 dataset, the first open dataset of ROCF line drawings.
  • To provide baseline experimental results using deep learning models for ROCF analysis.

Main Methods:

  • Developed a benchmarking framework for automatic ROCF test scoring.
  • Created the ROCFD528 dataset, comprising ROCF line drawings.
  • Evaluated state-of-the-art Convolutional Neural Networks (CNNs) using traditional and transfer learning.

Main Results:

  • The ROCFD528 dataset is the first open dataset for ROCF line drawings.
  • A CNN designed for sketches outperformed other CNN architectures in limited data scenarios (MAE = 3.448).
  • Established baseline performance metrics for deep learning models on ROCF analysis.

Conclusions:

  • The proposed framework and dataset facilitate fair comparison of automated ROCF scoring systems.
  • Sketch-optimized CNNs show promise for analyzing line drawings, especially with limited data.
  • This work serves as a model for developing robust machine learning tools for sketch and line drawing analysis.