【深度学习-图像识别】脑PET图像分析和疾病预测

比赛简介：Baseline：1. 读取数据2. 定义数据加载器：3. 构建模型：4. 训练模型：5. 生成预测结果： 最终提交结果：有需求的大佬欢迎加入我的接单群，需求详情请群里戳群主获取数据集：

这里提供一个baseline；

比赛简介：

比赛地址：http://challenge.xfyun.cn/topic/info?type=PET

数据长这个样子：

Baseline：

1. 读取数据

base = './train' count = 0 with open(os.path.join('train_list.txt'), 'w') as f: for i, cls_fold in enumerate(['AD', 'CN']): try: cls_base = os.path.join(base, cls_fold) files = os.listdir(cls_base) print('{} train num:'.format(cls_fold), len(files)) for pt in files: img = os.path.join(cls_fold, pt) info = img + ' ' + str(i) + '\n' f.write(info) count += 1 except Exception as e: continue print(count) with open(os.path.join('labels.txt'), 'w') as f: for i, cls_fold in enumerate(['AD', 'CN']): f.write(cls_fold+'\n') print('done.')

2. 定义数据加载器：

import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler from torch.utils.data import DataLoader import torchvision from torchvision.transforms import transforms from torchvision import models from torchvision.models import ResNet import numpy as np import matplotlib.pyplot as plt import os data_dir = './' train_dataset = torchvision.datasets.ImageFolder(root=os.path.join(data_dir, 'train'), transform=transforms.Compose( [ transforms.Resize(299), transforms.ToTensor(), transforms.Normalize( mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) ])) val_dataset = torchvision.datasets.ImageFolder(root=os.path.join(data_dir, 'train'), transform=transforms.Compose( [ transforms.Resize(299), transforms.ToTensor(), transforms.Normalize( mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) ])) train_dataloader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True) val_dataloader = DataLoader(dataset=val_dataset, batch_size=1, shuffle=True) # 类别名称 class_names = train_dataset.classes print('class_names:{}'.format(class_names)) # 训练设备 CPU/GPU device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print('trian_device:{}'.format(device.type))

3. 构建模型：

这里使用vgg16：

# -------------------------模型选择，优化方法， 学习率策略---------------------- model_name = 'vgg16' model = models.vgg16(pretrained=True) num_classes = 2 # 改变全连接层，2分类问题，out_features = 2 # model.aux_logits = False # model.AuxLogits.fc = nn.Linear(768, num_classes) # model.fc = nn.Linear(2048, num_classes) model.classifier[6] = nn.Linear(4096,num_classes) # 模型迁移到CPU/GPU model = model.to(device) # 定义损失函数 loss_fc = nn.CrossEntropyLoss() # 选择优化方法 optimizer = optim.Adam(model.parameters(), lr=2e-4) # 学习率调整策略 exp_lr_scheduler = lr_scheduler.StepLR(optimizer=optimizer, step_size=30, gamma=0.5) # step_size print('done.')

4. 训练模型：

# ----------------训练过程----------------- num_epochs = 101 for epoch in range(num_epochs): running_loss = 0.0 exp_lr_scheduler.step() for i, sample_batch in enumerate(train_dataloader): inputs = sample_batch[0] labels = sample_batch[1] model.train() # GPU/CPU inputs = inputs.to(device) labels = labels.to(device) optimizer.zero_grad() # foward outputs = model(inputs) # loss loss = loss_fc(outputs, labels) # loss求导，反向 loss.backward() # 优化 optimizer.step() # running_loss += loss.item() # 測試 if epoch % 5 == 0 and i == 1: correct = 0 total = 0 model.eval() for images_test, labels_test in val_dataloader: images_test = images_test.to(device) labels_test = labels_test.to(device) outputs_test = model(images_test) _, prediction = torch.max(outputs_test, 1) correct += (torch.sum((prediction == labels_test))).item() # print(prediction, labels_test, correct) total += labels_test.size(0) print('[{}, {}] running_loss = {:.5f} accurcay = {:.5f}'.format(epoch + 1, i + 1, running_loss / 20, correct / total)) running_loss = 0.0 print('training finish !') torch.save(model.state_dict(), './model.pth')

5. 生成预测结果：

test_base = './test/AD&CN' import pandas as pd import cv2 from PIL import Image from torchvision import transforms from torch.autograd import Variable as V import torch as t import pickle as pkl # model_name = 'vgg16_bn' trans=transforms.Compose( [ transforms.Resize(299), transforms.ToTensor(), transforms.Normalize( mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) ] ) model.eval() submission = {'uuid':[], 'label':[]} for i in tqdm(range(1, 1000+1)): im_pt = os.path.join(test_base, '{}.png'.format(i)) img = cv2.imread(im_pt) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = Image.fromarray(img) img = trans(img) img = img.unsqueeze(0) img = V(img.cuda()) predict = model(img) predict = F.softmax(predict)[0] temp = {'AD':float(predict[0]), 'CN':float(predict[1]), 'id':i} _, pred = torch.max(predict, 0) pred = class_names[int(pred)] submission['uuid'].append(i) submission['label'].append(pred) data = pd.DataFrame(submission) data.to_csv('./submission_{}.csv'.format(model_name), index=False) print('done.')

最终提交结果：

大概 0.71 左右；

有需求的大佬欢迎加入我的接单群，需求详情请群里戳群主

获取数据集：

关注我的公众号——可达鸭的深度学习教程，公众号内回复：“脑PET图像” 获取数据集：