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Resnet 50 Finetuned Brain Tumor
A brain tumor image classification model fine-tuned based on microsoft/resnet-50, achieving an accuracy of 91.71% on the evaluation set
Downloads 472
Release Time : 2/25/2023
Model Overview
This model is an image classification model fine-tuned based on the ResNet-50 architecture, specifically designed for the recognition and classification of brain tumor images.
Model Features
High Accuracy
Achieves an accuracy of 91.71% in brain tumor image classification tasks
Fine-tuning Optimization
Targeted fine-tuning based on the mature ResNet-50 architecture
Efficient Training
Utilizes Adam optimizer and linear learning rate scheduler for efficient training
Model Capabilities
Brain Tumor Image Classification
Medical Image Analysis
Use Cases
Medical Diagnosis
Brain Tumor Auxiliary Diagnosis
Assists doctors in preliminary screening and classification of brain tumors
Accuracy 91.71%
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