Comparison of Image Generation Techniques for Schizophrenia Detection Models

This dataset compares different image generation techniques used across various models within the repOD dataset for schizophrenia detection. The dataset is 5.5 KB in size and is stored in an XLS file format. It was authored by Md. Milon Hossain and last updated in March 2026.

Use Cases

• Compare the performance of Convolutional Neural Networks using different image generation techniques for enhancing SCZ detection.
• Analyze the impact of image generation methods on the explainability of models, referencing the Local Interpretable Model and Explainable Artificial Intelligence tags.
• Evaluate how different techniques affect the identification of critical brain regions, such as the Occipital Lobe, based on the associated tags.

Strengths

• The dataset is licensed under CC BY 4.0, allowing for open sharing and adaptation.
• It is focused on a specific application area: schizophrenia detection using brain imaging and AI models.
• The associated tags indicate a detailed scope covering brain lobes, diagnostic methods, and model explainability.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope and likely a summary or metadata file rather than primary data.
• Key structural details like the number of rows and columns are unknown, limiting understanding of its analytical utility.
• The description is minimal and repetitive, providing little context about the actual comparison metrics or results.

Provenance

Source

figshare, authored by Md. Milon Hossain.

Collection Method

Comparison of techniques within the repOD dataset; specific methodology is not detailed.

Freshness

Last updated on 2026-03-20.