使用Python实现深度学习模型：智能医疗影像识别与诊断

    pip install pandas numpy scikit-learn tensorflow keras matplotlib seaborn opencv-python

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    from tensorflow.keras.datasets import mnist
    
    # 加载数据
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    
    # 查看数据形状
    print(f"训练集形状: {X_train.shape}, 测试集形状: {X_test.shape}")
    
    # 显示一些样本图像
    plt.figure(figsize=(10, 5))
    for i in range(10):
    plt.subplot(2, 5, i+1)
    plt.imshow(X_train[i], cmap='gray')
    plt.title(f"Label: {y_train[i]}")
    plt.axis('off')
    plt.show()

    from tensorflow.keras.utils import to_categorical
    
    # 标准化图像数据
    X_train = X_train.astype('float32') / 255.0
    X_test = X_test.astype('float32') / 255.0
    
    # 将标签转换为独热编码
    y_train = to_categorical(y_train, 10)
    y_test = to_categorical(y_test, 10)

    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
    
    # 创建模型
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))
    
    # 编译模型
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

    # 训练模型
    history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
    
    # 评估模型
    loss, accuracy = model.evaluate(X_test, y_test)
    print(f'Loss: {loss}')
    print(f'Accuracy: {accuracy}')

    # 预测
    y_pred = model.predict(X_test)
    y_pred_classes = np.argmax(y_pred, axis=1)
    y_true = np.argmax(y_test, axis=1)
    
    # 打印预测结果
    print(y_pred_classes[:10])
    print(y_true[:10])

    # 可视化训练过程
    plt.figure(figsize=(12, 4))
    
    plt.subplot(1, 2, 1)
    plt.plot(history.history['loss'], label='Training Loss')
    plt.plot(history.history['val_loss'], label='Validation Loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.title('Training and Validation Loss')
    
    plt.subplot(1, 2, 2)
    plt.plot(history.history['accuracy'], label='Training Accuracy')
    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.title('Training and Validation Accuracy')
    
    plt.show()
    
    # 混淆矩阵
    from sklearn.metrics import confusion_matrix
    import seaborn as sns
    
    cm = confusion_matrix(y_true, y_pred_classes)
    plt.figure(figsize=(10, 8))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
    plt.xlabel('Predicted')
    plt.ylabel('Actual')
    plt.title('Confusion Matrix')
    plt.show()