РАСПОЗНАВАНИЕ МИКРОСКОПИЧЕСКИХ ИЗОБРАЖЕНИЙ ПЫЛЬЦЕВЫХ ЗЕРЕН С ПОМОЩЬЮ СВЕРТОЧНОЙ НЕЙРОННОЙ СЕТИ VGG-16

The article presents the result of an experiment on the application of transfer learning using the Visual Geometry Group with 16 layers (VGG-16) convolutional neural network in relation to the problem of recognizing pollen grains in images. An analysis of the information-theoretical base on the application of machine learning algorithms to the problem of classifying pollen grains over the past few years has shown the need to develop (apply) a new method for recognizing images of pollen grains obtained using an optical microscope. Currently, automatic classification for pollen identification is becoming a very active area of research. The article substantiates the task of automating the classification of pollen grains. The aim of the study is to analyze the efficiency and accuracy of classifying microscopic images of pollen grains using transfer learning of the VGG-16 convolutional neural network. Transfer learning was performed using the VGG-16 neural network, which has 13 convolutional layers grouped into 5 blocks with pooling and 3 smoothing layers at the output. Since transfer learning is used, the number of training epochs can be chosen to be small. For this network, only the smoothing output layers change, and the feature extraction remains the same as in the classical model. Therefore, it was chosen to use 10 training epochs. Other hyperparameters are as follows: Drop Out regularization with a probability of 50%, optimization method is ADAM, activation function is sigmoid, loss function is cross-entropy, batch size is 32 images. As a result, by adjusting the hyperparameters of the model and using augmentation, it was possible to achieve a share of correct recognitions of about 80%. At the same time, due to the different number of training examples, the particular characteristics of the classes differ somewhat. Thus, the maximum precision and recall reach 94% and 83%, respectively, for the Dandelion type. In the future, studies are planned to use augmentation as a preprocessing to create a balanced sample. By applying the VGG-16 convolutional neural network to the problem of pollen grain image recognition, high accuracy and efficiency of the method were achieved.В статье приводится результат эксперимента по применению трансферного обучения с помощью сверточной нейронной сети Visual Geometry Group with 16 layers (VGG-16) применительно к задаче распознавания пыльцевых зерен на изображениях. Анализ информационно-теоретической базы по применению алгоритмов машинного обучения к задаче классификации пыльцевых зерен за последние несколько лет показал необходимость разработки (применения) нового метода к распознаванию изображений пыльцевых зерен, полученных с помощью оптического микроскопа. В настоящее время автоматическая классификация для идентификации пыльцы становится очень активной областью исследований. В статье обоснована задача автоматизации классификации пыльцевых зерен. Целью исследования является анализ эффективности и точности классификации микроскопических изображений пыльцевых зерен с помощью трансферного обучения сверточной нейронной сети VGG-16. Трансферное обучение было выполнено с помощью нейронной сети VGG-16, имеющей 13 сверточных слоев, группируемых в 5 блоков с пулингом и 3 сглаживающих слоя на выходе. Поскольку применяется трансферное обучение, то количество эпох обучения можно выбрать небольшим. У данной сети меняются только сглаживающие выходные слои, а извлечение признаков осуществляется с весами классической модели. Поэтому было выбрано использовать 10 эпох обучения. Другие гиперпараметры – регуляризация Drop Out с вероятностью 50 %, метод оптимизации – ADAM, функция активации – sigmoid, функция потерь – кросс-энтропия, размер батча – 32 изображения. В результате за счет настройки гиперпараметров модели и использования аугментаций удалось достичь доли верных распознаваний порядка 80 %. При этом в связи с разным количеством обучающих примеров частные характеристики по классам несколько отличаются. Так, максимальные точность и полнота достигают 94 и 83 % соответственно для типа Одуванчик. В будущем планируются исследования для применения аугментации в качестве предобработки для создания сбалансированной выборки. За счет применения сверточной нейронной сети VGG-16 к задаче распознаваний изображений пыльцевых зерен были достигнуты высокие показатели точности и эффективности метода

Novel semi-supervised classification method based on class certainty of samples

The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labeled samples. However, the number of labeled samples is limited due to the expensive and time-consuming collection. To effectively utilize the information of unlabeled samples in the learning process, this paper proposes a novel semi-supervised classification method based on class certainty of samples (CCS). First, the class certainty of unlabeled samples obtained based on multi-class SVM is smoothed for robustness. Then, a new semi-supervised linear discriminant analysis (LDA) is presented based on class certainty, which improves the separability of samples in the projection subspace. Ultimately, we extend the semi-supervised LDA to nonlinear dimensional reduction by combining class certainty and kernel methods. Furthermore, to assess the effectiveness of proposed method, the nearest neighbor classifier is adopted to classify actual SAR images. The results demonstrate that the proposed method can effectively exploit the information of unlabeled samples and greatly improve the classification effect compared with other state-of-the-art approaches

Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis

Breast cancer is the second most common cancer among women worldwide. Diagnosis of breast cancer by the pathologists is a time-consuming procedure and subjective. Computer aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to a higher accuracy in the classification task and dimension reduction plays an important role. Therefore, different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. To this purpose, we have proposed reduced deep convolutional activation features (R-DeCAF). In this framework, pre-trained CNNs such as AlexNet, VGG-16 and VGG-19 are utilized in transfer learning mode as feature extractors. DeCAF features are extracted from the first fully connected layer of the mentioned CNNs and support vector machine has been used for binary classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to a higher accuracy in the classification task using small number of features considering specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis dataset. Comprehensive results show improvement in the classification accuracy up to 4.3% with less computational time. Best achieved accuracy is 91.13% for 400x data with feature vector size (FVS) of 23 and CEV equals to 0.15 using pre-trained AlexNet as feature extractor and PCA as feature reduction algorithm