Research on Brain Tumor Classification Method Based on Improved ResNet34 Network

2025-12-03 · Yufeng Li, Wenchao Zhao, Bo Dang, Weimin Wang arxiv

Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.

📄 PDF Abstract BibTeX arXiv:2512.03751

Code (0)

등록된 구현이 없습니다.

Tasks

Brain Tumor Classification Image Classification

Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification

2024-11-23 · Md Ashik Khan, Rafath Bin Zafar Auvee

Accurate brain tumor classification in MRI images is critical for timely diagnosis and treatment planning. While deep learning models like ResNet-18, VGG-16 have shown high accuracy, they often come with increased comple…

Binary Classification Brain Tumor Classification Classification Few-Shot Learning+1

Accelerating Cerebral Diagnostics with BrainFusion: A Comprehensive MRI Tumor Framework

2025-09-29 · Walid Houmaidi, Youssef Sabiri, Salmane El Mansour Billah, Amine Abouaomar arxiv

The early and accurate classification of brain tumors is crucial for guiding effective treatment strategies and improving patient outcomes. This study presents BrainFusion, a significant advancement in brain tumor analys…

Brain Tumor Detection Using Deep Learning Approaches

2023-09-21 · Razia Sultana Misu

Brain tumors are collections of abnormal cells that can develop into masses or clusters. Because they have the potential to infiltrate other tissues, they pose a risk to the patient. The main imaging technique used, MRI,…

Deep Learning Transfer Learning

Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning

2025-07-09 · Daniel Onah, Ravish Desai arxiv

In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In …

Brain Tumor Classification Computational Efficiency Transfer Learning

Classification of Brain Tumors using Hybrid Deep Learning Models

2025-08-02 · Neerav Nemchand Gala arxiv

The use of Convolutional Neural Networks (CNNs) has greatly improved the interpretation of medical images. However, conventional CNNs typically demand extensive computational resources and large training datasets. To add…

Transfer Learning