Trade-Off Analysis of Classical Machine Learning and Deep Learning Models for Robust Brain Tumor Detection: Benchmark Study

The study reveals meaningful trade-offs between model complexity, annotation requirements, and deployment feasibility-critical factors for selecting models in real-world medical imaging applications.

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Publication Details

Journal
JMIR AI
Published
2025-08-26
DOI
https://doi.org/10.2196/76344
Citations
2
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
1.68
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article

Trade-Off Analysis of Classical Machine Learning and Deep Learning Models for Robust Brain Tumor Detection: Benchmark Study

Yujia Tian
2 citations
JMIR AI
Brain Tumor Detection and Classification
1.68
article

Trade-Off Analysis of Classical Machine Learning and Deep Learning Models for Robust Brain Tumor Detection: Benchmark Study

Yujia Tian
article en
2 citations

Abstract

The study reveals meaningful trade-offs between model complexity, annotation requirements, and deployment feasibility-critical factors for selecting models in real-world medical imaging applications.

JMIR AIVol. 4
Dartmouth College (US)
Openalex Percentile: Top 14%
Brain Tumor Detection and Classification
1.68
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Trade-Off Analysis of Classical Machine Learning and Deep Learning Models for Robust Brain Tumor Detection: Benchmark Study — Yujia Tian · JMIR AI (2025) | TGRS Research Map | TGRS