Automated brain tumor detection in MRI images using CNN and ResNet architectures
Summary
This paper presents an automated deep learning approach for brain tumor detection in MRI images using CNN and ResNet architectures with transfer learning, achieving up to 97% accuracy.
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Cached at: 06/29/26, 05:28 AM
# Automated brain tumor detection in MRI images using CNN and ResNet architectures Source: [https://arxiv.org/abs/2606.27405](https://arxiv.org/abs/2606.27405) [View PDF](https://arxiv.org/pdf/2606.27405) > Abstract:Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans\. Accurate and early diagnosis of brain tumors remains challenging due to the complexity of brain structures and reliance on manual interpretation\. This work presents an automated deep learning\-based approach for brain tumor detection from MRI images using Convolutional Neural Networks and Residual Networks\. Transfer learning is applied with two pretrained architectures, ResNet18 and ResNet50, to classify MRI scans into tumor and non\-tumor categories\. Experiments are conducted on a dataset of 3,929 brain MRI images, evaluating the impact of model depth and fine\-tuning strategies\. The results show that ResNet18 achieves a higher accuracy of 97% compared to 96% for ResNet50, demonstrating better generalization on limited medical data\. The proposed framework enables fast, accurate, and cost\-effective brain tumor detection, supporting early diagnosis and clinical decision\-making\. ## Submission history From: K Paramesha Dr\. \[[view email](https://arxiv.org/show-email/576b2c99/2606.27405)\] **\[v1\]**Thu, 25 Jun 2026 04:41:19 UTC \(938 KB\)
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