毕设项目分享 基于卷积神经网络的乳腺癌分类 深度学习 医学图像

    dataset train
      benign
       b1.jpg
       b2.jpg
       //
      malignant
       m1.jpg
       m2.jpg
       //  validation
       benign
    b1.jpg
    b2.jpg
    //
       malignant
    m1.jpg
    m2.jpg
    //...

    import json
    import math
    import os
    import cv2
    from PIL import Image
    import numpy as np
    from keras import layers
    from keras.applications import DenseNet201
    from keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, TensorBoard
    from keras.preprocessing.image import ImageDataGenerator
    from keras.utils.np_utils import to_categorical
    from keras.models import Sequential
    from keras.optimizers import Adam
    import matplotlib.pyplot as plt
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import cohen_kappa_score, accuracy_score
    import scipy
    from tqdm import tqdm
    import tensorflow as tf
    from keras import backend as K
    import gc
    from functools import partial
    from sklearn import metrics
    from collections import Counter
    import json
    import itertools

    def Dataset_loader(DIR, RESIZE, sigmaX=10):
    IMG = []
    read = lambda imname: np.asarray(Image.open(imname).convert("RGB"))
    for IMAGE_NAME in tqdm(os.listdir(DIR)):
        PATH = os.path.join(DIR,IMAGE_NAME)
        _, ftype = os.path.splitext(PATH)
        if ftype == ".png":
            img = read(PATH)
           
            img = cv2.resize(img, (RESIZE,RESIZE))
           
            IMG.append(np.array(img))
    return IMG
    
    benign_train = np.array(Dataset_loader('data/train/benign',224))
    malign_train = np.array(Dataset_loader('data/train/malignant',224))
    benign_test = np.array(Dataset_loader('data/validation/benign',224))
    malign_test = np.array(Dataset_loader('data/validation/malignant',224))

    benign_train_label = np.zeros(len(benign_train))
    malign_train_label = np.ones(len(malign_train))
    benign_test_label = np.zeros(len(benign_test))
    malign_test_label = np.ones(len(malign_test))
    
    X_train = np.concatenate((benign_train, malign_train), axis = 0)
    Y_train = np.concatenate((benign_train_label, malign_train_label), axis = 0)
    X_test = np.concatenate((benign_test, malign_test), axis = 0)
    Y_test = np.concatenate((benign_test_label, malign_test_label), axis = 0)
    
    s = np.arange(X_train.shape[0])
    np.random.shuffle(s)
    X_train = X_train[s]
    Y_train = Y_train[s]
    
    s = np.arange(X_test.shape[0])
    np.random.shuffle(s)
    X_test = X_test[s]
    Y_test = Y_test[s]
    
    Y_train = to_categorical(Y_train, num_classes= 2)
    Y_test = to_categorical(Y_test, num_classes= 2)

    x_train, x_val, y_train, y_val = train_test_split(
    X_train, Y_train, 
    test_size=0.2, 
    random_state=11
    )
    
    w=60
    h=40
    fig=plt.figure(figsize=(15, 15))
    columns = 4
    rows = 3
    
    for i in range(1, columns*rows +1):
    ax = fig.add_subplot(rows, columns, i)
    if np.argmax(Y_train[i]) == 0:
        ax.title.set_text('Benign')
    else:
        ax.title.set_text('Malignant')
    plt.imshow(x_train[i], interpolation='nearest')
    plt.show()

    BATCH_SIZE = 16
    
    train_generator = ImageDataGenerator(
        zoom_range=2,  # 设置范围为随机缩放
        rotation_range = 90,
        horizontal_flip=True,  # 随机翻转图片
        vertical_flip=True,  # 随机翻转图片
    )

    def build_model(backbone, lr=1e-4):
    model = Sequential()
    model.add(backbone)
    model.add(layers.GlobalAveragePooling2D())
    model.add(layers.Dropout(0.5))
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(2, activation='softmax'))
    
    model.compile(
        loss='binary_crossentropy',
        optimizer=Adam(lr=lr),
        metrics=['accuracy']
    )
    return model
    
    resnet = DenseNet201(
    weights='imagenet',
    include_top=False,
    input_shape=(224,224,3)
    )
    
    model = build_model(resnet ,lr = 1e-4)
    model.summary()

    learn_control = ReduceLROnPlateau(monitor='val_acc', patience=5,
                                  verbose=1,factor=0.2, min_lr=1e-7)
    
    filepath="weights.best.hdf5"
    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
    
    history = model.fit_generator(
    train_generator.flow(x_train, y_train, batch_size=BATCH_SIZE),
    steps_per_epoch=x_train.shape[0] / BATCH_SIZE,
    epochs=20,
    validation_data=(x_val, y_val),
    callbacks=[learn_control, checkpoint]
    )

    history_df = pd.DataFrame(history.history)
    history_df[['loss', 'val_loss']].plot()
    
    history_df = pd.DataFrame(history.history)
    history_df[['acc', 'val_acc']].plot()

    from sklearn.metrics import classification_report
    classification_report( np.argmax(Y_test, axis=1), np.argmax(Y_pred_tta, axis=1))
    
    from sklearn.metrics import confusion_matrix
    
    def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')
    
    print(cm)
    
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=55)
    plt.yticks(tick_marks, classes)
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")
    
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()
    
    cm = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(Y_pred, axis=1))
    
    cm_plot_label =['benign', 'malignant']
    plot_confusion_matrix(cm, cm_plot_label, title ='Confusion Metrix for Skin Cancer')