# Deep Learning-Based Automatic Brain Tumor MRI Classification System: A Comparative Study of Three Transfer Learning Architectures

> This article introduces a deep learning-based automatic classification system for brain tumor MRI images, comparing the performance of three transfer learning architectures—VGG16, ResNet50, and EfficientNetB0—in medical image diagnosis. ResNet50 achieved a classification accuracy of 98.9%.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-10T23:45:27.000Z
- 最近活动: 2026-06-10T23:49:53.526Z
- 热度: 141.9
- 关键词: 深度学习, 医学影像, 脑肿瘤分类, 迁移学习, ResNet50, MRI, TensorFlow, 计算机辅助诊断
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## [Introduction] Study on Deep Learning-Based Automatic Brain Tumor MRI Classification System: A Comparison of Three Transfer Learning Architectures

This article presents a study on a deep learning-based automatic classification system for brain tumor MRI images, comparing the performance of three transfer learning architectures: VGG16, ResNet50, and EfficientNetB0. Using a public dataset, ResNet50 achieved a classification accuracy of 98.9%. The study aims to assist doctors in making fast and accurate diagnoses, providing practical references for AI applications in medical imaging.

## Research Background and Significance

Early diagnosis of brain tumors has a decisive impact on patients' survival rates and quality of life. Traditional MRI image diagnosis relies on radiologists' experience, which is time-consuming, labor-intensive, and subject to subjective differences. Deep learning technology has promoted the development of computer-aided diagnosis. This study focuses on automatic classification of brain tumor MRI to assist doctors in decision-making, providing valuable references for AI applications in medical imaging.

## Dataset and Research Methods

**Dataset**: Adopted the public Brain Tumor MRI Dataset, containing 7023 images divided into four categories: glioma, meningioma, pituitary tumor, and no tumor.
**Transfer Learning Architectures**: Compared three pre-trained CNN architectures:
- VGG16: Fine-tuned at layer 15, added global average pooling, fully connected layers, and Dropout;
- ResNet50: Fine-tuned at layer 143, used residual connections to solve gradient vanishing;
- EfficientNetB0: Fine-tuned at layer 150, balanced model parameters and accuracy through compound scaling.
**Preprocessing and Augmentation**: Uniform image size and pixel normalization; data augmentation included random rotation, brightness adjustment, and histogram equalization (experimental).

## Experimental Results and Performance Analysis

Accuracy comparison of three models:
|Model|Accuracy|
|---|---|
|VGG16|97.3%|
|VGG16 + Histogram Equalization|97.6%|
|ResNet50|98.9%|
|EfficientNetB0|93.4%|
**Key Findings**: ResNet50 performed best, with perfect classification for no tumor and pituitary tumor cases; histogram equalization had limited improvement on VGG16; EfficientNetB0 had difficulty distinguishing between glioma and meningioma.

## Application Value and Future Outlook

**Application Value**: Provided a second opinion for radiologists, assisted primary doctors in under-resourced medical areas with screening, and improved diagnostic efficiency and consistency.
**Future Directions**: Extend tumor segmentation functionality, introduce explainable AI (Grad-CAM), conduct multi-center clinical validation, develop web-based diagnostic tools, and integrate with hospital PACS systems.

## Summary and Insights

This study verified the potential of transfer learning in medical image analysis, with ResNet50 performing excellently in brain tumor classification tasks. The study revealed fine-grained classification differences among different architectures, providing references for subsequent research. It also offers a complete practical case for medical AI developers, and AI-assisted diagnostic tools will play an important role in future medical practice.