definedet网络对CT图像进行目标检测深度学习代码

以下是使用PyTorch实现的Definedet网络对CT图像进行目标检测的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F


class Definedet(nn.Module):
    def __init__(self, num_classes=2):
        super(Definedet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(256)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(512)
        self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(1024)
        self.conv7 = nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1)
        self.bn7 = nn.BatchNorm2d(512)
        self.conv8 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)
        self.bn8 = nn.BatchNorm2d(256)
        self.conv9 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.bn9 = nn.BatchNorm2d(128)
        self.conv10 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1)
        self.bn10 = nn.BatchNorm2d(64)
        self.conv11 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
        self.bn11 = nn.BatchNorm2d(32)
        self.conv12 = nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1)
        self.bn12 = nn.BatchNorm2d(16)
        self.conv13 = nn.Conv2d(16, 8, kernel_size=3, stride=1, padding=1)
        self.bn13 = nn.BatchNorm2d(8)
        self.conv14 = nn.Conv2d(8, 4, kernel_size=3, stride=1, padding=1)
        self.bn14 = nn.BatchNorm2d(4)
        self.conv15 = nn.Conv2d(4, num_classes, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = F.relu(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = F.relu(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = F.relu(x)
        x = self.conv6(x)
        x = self.bn6(x)
        x = F.relu(x)
        x = self.conv7(x)
        x = self.bn7(x)
        x = F.relu(x)
        x = self.conv8(x)
        x = self.bn8(x)
        x = F.relu(x)
        x = self.conv9(x)
        x = self.bn9(x)
        x = F.relu(x)
        x = self.conv10(x)
        x = self.bn10(x)
        x = F.relu(x)
        x = self.conv11(x)
        x = self.bn11(x)
        x = F.relu(x)
        x = self.conv12(x)
        x = self.bn12(x)
        x = F.relu(x)
        x = self.conv13(x)
        x = self.bn13(x)
        x = F.relu(x)
        x = self.conv14(x)
        x = self.bn14(x)
        x = F.relu(x)
        x = self.conv15(x)
        return x

这里使用了一个简单的卷积神经网络，包括15个卷积层和batch normalization层，最后输出指定数量的类别（这里是2类）。这个模型可以用于对CT图像中的目标进行检测。