在考虑深度倾斜方法时,我的ROC曲线有什么问题?

我有一个应用程序,用CNN对图像进行分类。我正在考虑在64 x 64图像块的输入大小上使用Mobilenet、Resnet和Densenet。为了对图像进行分类,我将其类定义为对其块进行分类时最常见的类。问题是高度不平衡的,我的阳性样本比阴性样本多。我正在考虑三个数据集。

我觉得很奇怪的是,获得50%标准化准确度的方法也得到了0.85、0.90甚至0.97 AUC。最后一个AUC似乎来自一个近乎完美的分类器,但如果其标准化精度为50%,这怎么可能呢?

我的问题是不平衡的。那么,主要在我的数据集中发现的阳性样本,以及我的ROC兴趣类别是否影响结果?

2-我使用块的平均分数作为图像的分数。这是解决这个问题的方法吗?

下面是我用来生成标签和分数的代码(PYTHON)

 base_model=MobileNet(input_shape (64,64,3),weights=None,include_top=False)
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(64, activation='relu')(x)
    predictions = Dense(2, activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    model.load_weights(model_path)

    intermediate_layer_model = Model(inputs=model.input, outputs=model.get_layer("dense_2").output)
    print("Loaded model from disk")
    intermediate_layer_model.compile(loss='categorical_crossentropy', optimizer=algorithm, metrics=['accuracy'])

    #read images, divide them into blocks, predict images and define the mean scores as the score for an image
    with open(test_images_path) as f:
            images_list = f.readlines()
            images_name = [a.strip() for a in images_list]
            predicted_image_vector = []
            groundtruth_image_vector = []

            for line in images_name:
                x_test=[]
                y_test=[]
                print(line)
                image = cv2.imread(line,1)
                #divide into blocks
                windows = view_as_windows(image, (64,64,3), step=64)

                #prepare blocks to be tested later 
                for i in range(windows.shape[0]):
                    for j in range(windows.shape[1]):
                            block=np.squeeze(windows[i,j])
                            x_test.append(block)
                            label = du.define_class(line)
                            y_test.append(label)

            #predict scores for all blocks in the current test image
            intermediate_output = intermediate_layer_model.predict(np.asarray(x_test), batch_size=32, verbose=0)
            #the score for an image is the mean score of its blocks
            prediction_current_image=np.mean(intermediate_output, axis=0)
            predicted_image_vector.append(prediction_current_image)
 groundtruth_image_vector.append(np.argmax(np.bincount(np.asarray(y_test))))

    predicted_image_vector=np.array(predicted_image_vector)
    groundtruth_image_vector=np.array(groundtruth_image_vector)
    print("saving scores and labels to plot ROC curves")

    np.savetxt(dataset_name+ '-scores.txt', predicted_image_vector, delimiter=',') 
    np.savetxt(dataset_name+ '-labels.txt', groundtruth_image_vector, delimiter=',') 

下面是我用来生成ROC曲线的代码(MATLAB)

function plot_roc(labels_file, scores_file, name_file, dataset_name)

    format longG
    label=dlmread(labels_file);
    scores=dlmread(scores_file);
    [X,Y,T,AUC] = perfcurve(label,scores(:,2),1);   

    f=figure()
    plot(X,Y);
    title(['ROC Curves for Mobilenet in ' dataset_name])
    xlabel('False positive rate'); 
    ylabel('True positive rate');
    txt = {'Area Under the Curve:', AUC};
    text(0.5,0.5,txt)
    saveas(f, name_file);
    disp("ok")



end