Medical image recognition based on PyTorch and transfer learning
The application of deep learning in the field of medical imaging has achieved important breakthroughs. This article will introduce how to use PyTorch and transfer learning to implement medical image recognition tasks. We will demonstrate the entire process through a concrete example and provide the corresponding source code.
First, we need to prepare the dataset. Here we assume that there is already a dataset containing medical imaging data, which includes different categories (e.g., tumors and normal tissues). Make sure the data set has been correctly divided into training and test sets.
Next, we will build a deep learning model using PyTorch. In this example, we will use transfer learning, which leverages a model pretrained on a large-scale image dataset as a basis, and apply it to a medical image recognition task. This approach can speed up model training and improve performance.
First, we need to import the required libraries:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torchvision