{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T03:50:20Z","timestamp":1785901820154,"version":"3.56.0"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate brain tumor classification from MRI is often constrained by limited labeled data. We systematically compare conventional machine learning, deep learning, and few-shot learning (FSL) for four classes (glioma, meningioma, pituitary, no tumor) using a standardized pipeline. Models are trained on the Kaggle Brain Tumor MRI Dataset and evaluated across dataset regimes (100%\u219210%). We further test generalization on BraTS and quantify robustness to resolution changes, acquisition noise, and modality shift (T1\u2192FLAIR). To support clinical trust, we add visual explanations (Grad-CAM\/saliency) and report per-class results (confusion matrices). A fairness-aligned protocol (shared splits, optimizer, early stopping) and a complexity analysis (parameters\/FLOPs) enable balanced comparison. With full data, Convolutional Neural Networks (CNNs)\/Residual Networks (ResNets) perform strongly but degrade with 10% data; Model-Agnostic Meta-Learning (MAML) retains competitive performance (AUC-ROC \u2265 0.9595 at 10%). Under cross-dataset validation (BraTS), FSL\u2014particularly MAML\u2014shows smaller performance drops than CNN\/ResNet. Variability tests reveal FSL\u2019s relative robustness to down-resolution and noise, although modality shift remains challenging for all models. Interpretability maps confirm correct activations on tumor regions in true positives and explain systematic errors (e.g., \u201cno tumor\u201d\u2192pituitary). Conclusion: FSL provides accurate, data-efficient, and comparatively robust tumor classification under distribution shift. The added per-class analysis, interpretability, and complexity metrics strengthen clinical relevance and transparency.<\/jats:p>","DOI":"10.3390\/a18100624","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T08:20:28Z","timestamp":1759393228000},"page":"624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Evaluating Machine Learning Techniques for Brain Tumor Detection with Emphasis on Few-Shot Learning Using MAML"],"prefix":"10.3390","volume":"18","author":[{"given":"Soham Sanjay","family":"Vaidya","sequence":"first","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9068-2337","authenticated-orcid":false,"given":"Raja Hashim","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany"},{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Faiz","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7863-3746","authenticated-orcid":false,"given":"Iftikhar","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talha Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105337","DOI":"10.1016\/j.imavis.2024.105337","article-title":"ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification","volume":"154","author":"Ouahab","year":"2025","journal-title":"Image Vis. 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