ArticleBioengineering (Basel, Switzerland)2025
Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.Sensors (Basel, Switzerland) · 2025Article
- Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.Scientific reports · 2025Article
- Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.Sensors (Basel, Switzerland) · 2025Article
- V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model.Scientific reports · 2025Article
- Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (AP
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.