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Medical
M
Medical
Developed by subh71
A medical image classification model generated using the PyTorch framework and HuggingPics tool, designed to identify different types of lung tissue images.
Downloads 63
Release Time : 2/11/2024
Model Overview
This model specializes in medical image classification tasks, accurately distinguishing between lung image types such as adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal tissue.
Model Features
High Accuracy
Achieves 95.6% accuracy in medical image classification tasks.
Ease of Use
Automatically generated via the HuggingPics tool for quick deployment and usage.
Specialized Application
Specifically designed for medical imaging, suitable for assisting in lung disease diagnosis.
Model Capabilities
Medical Image Classification
Lung Tissue Identification
Cancer Type Differentiation
Use Cases
Medical Diagnosis
Lung Cancer Screening
Assists doctors in identifying cancer types in lung tissue images.
Can differentiate between various cancer types such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma.
Pathological Analysis Assistance
Provides preliminary image classification results for pathologists.
With an accuracy rate of 95.6%, it reduces the workload of manual screening.
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