# Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN

arXiv: https://arxiv.org/abs/2509.05821 · HTML: https://mlsanity.com/paper/2509.05821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks](https://mlsanity.com/paper/1709.00382/) (2017)
- [Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries](https://mlsanity.com/paper/1811.00597/) (2018)

## Abstract (arXiv)

Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. Brain health is critical to human life, and accurate diagnosis is essential for effective treatment. Magnetic Resonance Imaging (MRI) provides key data for brain tumor detection, serving as a major source of big data for AI-driven image classification. In this study, we classified glioma, meningioma, and pituitary tumors from MRI images using Region-based Convolutional Neural Network (R-CNN) and UNet architectures. We also applied Convolutional Neural Networks (CNN) and CNN-based transfer learning models such as Inception-V3, EfficientNetB4, and VGG19. Model performance was assessed using F-score, recall, precision, and accuracy. The Fast R-CNN achieved the best results with 99% accuracy, 98.5% F-score, 99.5% Area Under the Curve (AUC), 99.4% recall, and 98.5% precision. Combining R-CNN, UNet, and transfer learning enables earlier diagnosis and more effective treatment in IoT-healthcare systems, improving patient outcomes. IoT devices such as wearable monitors and smart imaging systems continuously collect real-time data, which AI algorithms analyze to provide immediate insights for timely interventions and personalized care. For external cohort cross-dataset validation, EfficientNetB2 achieved the strongest performance among fine-tuned EfficientNet models, with 92.11% precision, 92.11% recall/sensitivity, 95.96% specificity, 92.02% F1-score, and 92.23% accuracy. These findings underscore the robustness and reliability of AI models in handling diverse datasets, reinforcing their potential to enhance brain tumor classification and patient care in IoT healthcare environments.