Imbalanced Image Classification Research Articles

Imbalanced image classification algorithm based on fine-grained analysis

Fine-grained attribute analysis and data imbalance have always been research hotspots in the field of computer vision. Due to the complexity and diversity of fine-grained attribute images, traditional image classification methods have shortcomings in paying attention to fine-grained attributes of images and perform poorly when dealing with imbalanced data sets. To overcome these problems, this study proposes a fine-grained image threshold classification algorithm based on deep metric learning. By introducing a metric learning method, the focus on fine-grained attributes of images is enhanced. At the same time, by applying pairwise loss and proxy loss, the classification accuracy of the model is improved and the model convergence speed is accelerated. In order to deal with the problem of data imbalance, a classifier based on threshold analysis is designed. The classifier uses threshold analysis technology to achieve multi-level classification of fine-grained images, thereby improving the problem of low classification accuracy of a few categories in imbalanced data sets. Experimental results show that the proposed fine-grained image threshold classification algorithm based on deep metric learning is significantly superior to other methods in terms of classification accuracy.

Adaptive unified contrastive learning with graph-based feature aggregator for imbalanced medical image classification

Medical image datasets are often imbalanced due to biases in data collection and limitations in acquiring data for rare conditions. Addressing class imbalance is crucial for developing reliable deep-learning algorithms capable of effectively handling all classes. Recent class imbalanced methods have investigated the effectiveness of self-supervised learning (SSL) and demonstrated that such learned features offer increased resilience to class imbalance issues and obtain much improved performances over other types of class imbalanced methods. However, existing SSL methods either lack end-to-end capabilities or require substantial memory resources, potentially resulting in sub-optimal features and classifiers and limiting their practical usage. Moreover, the conventional pooling operations (e.g., max-pooling, or average-pooling) tend to generate less discriminative features when datasets pose high inter-class similarities. To alleviate the above issues, in this study, we present a novel end-to-end self-supervised learning framework tailored for imbalanced medical image datasets. Our framework constitutes an adaptive contrastive loss that can dynamically adjust the model’s learning focus between feature learning and classifier learning and a feature aggregation mechanism based on Graph Neural Networks to further enhance feature discriminability. We evaluate the effectiveness of our framework on four medical datasets, and the experimental results highlight its superior performance in imbalanced image classification tasks.

A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data

This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced image classification. The application of SMOTE in imbalanced picture datasets is still underexplored, even though CNNs have been shown to be successful in image classification and that ensemble learning approaches have improved their performance. To investigate whether or not SMOTE can increase classification accuracy and other performance measures when combined with CNN-based classifiers, our research makes use of a CIFAR-10 dataset that has been artificially step-imbalanced and has varying imbalanced ratios. We conducted experiments using five distinct models, namely AdaBoost, XGBoost, standalone CNN, CNN-AdaBoost, and CNN-XGBoost, on datasets that were either imbalanced or SMOTE-balanced. Metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were included in the evaluation process. The findings indicate that SMOTE dramatically improves the accuracy of minority classes, and that the combination of ensemble classifiers with CNNs and oversampling techniques significantly improves overall classification performance, particularly in situations when there is a high-class imbalance. When it comes to enhancing imbalanced classification tasks, this study demonstrates the potential of merging oversampling techniques with CNN-based ensemble classifiers to minimize the impacts of class imbalance in picture datasets. This suggests a promising direction for future research in this area.

An empirical study of deep learning-based feature extractor models for imbalanced image classification

An empirical study of deep learning-based feature extractor models for imbalanced image classification

A new hierarchical algorithm based on CapsGAN for imbalanced image classification

AbstractImbalanced image datasets consist of image datasets where there is a significant disparity in the number of samples across different classes. With imbalanced image datasets, learning algorithms often tend to be biased toward the majority class samples. This leads to poor classification of minority class samples as their training is not properly conducted. It becomes more complicated when the number of samples in the minority class is very low. In this paper, a novel hierarchical algorithm is proposed for generating new data using Capsule Generative Adversarial Networks (CapsGAN) to address the class imbalance problem in imbalanced image datasets. Unlike common GAN models, the proposed method incorporates an auxiliary CapsNet to identify high‐value images in both minority and majority classes. This identification is based on the ability to detect complex relationships between low‐level and high‐level features present in capsule networks. Furthermore, the proposed CapsGAN model is conditioned to generate minority class samples based on feature vectors of last capsule layer to achieve a more balanced image dataset. For evaluating the performance of the proposed model, an image dataset called CICS was collected and introduced. Extensive experiments were also conducted using various online image datasets from different domains, with varying numbers of classes and data sizes. The experimental results demonstrated that the proposed model can generate high‐quality samples in cases where the image dataset or the number of minority class samples is relatively small. Furthermore, the proposed model has maintained an accuracy of over 80% in an imbalanced ratio of 1:60.

Similar classes latent distribution modelling-based oversampling method for imbalanced image classification

Learning an unbiased classifier from imbalanced image datasets is challenging since the classifier may be strongly biased toward the majority class. To address this issue, some generative model-based oversampling methods have been proposed. However, most of these methods pay little attention to boundary samples, which may contribute tiny to learning an unbiased classifier. In this paper, we focus on boundary samples and propose a similar classes latent distribution modelling-based oversampling method. Specifically, first, we model each class as different von Mises–Fisher distributions, thereby aligning feature learning with the class distributions. Furthermore, we develop a distance minimization loss function, which makes latent representations from similar classes close to each other. In this way, the generator can capture more shared features during training. In addition, we propose a boundary sampling strategy, which uses latent variables near the decision boundary to generate boundary samples. These samples expand the minority decision region and reshape the decision boundary. Experiments on four imbalanced image datasets show that the proposed method achieves promising performance in terms of Recall, Precision, F1-score, and G-mean.

Deep active learning models for imbalanced image classification

Active learning can query valuable samples in an unlabeled sample pool for annotation, thus building a more informative labeled dataset and reducing the annotation cost. However, traditional active learning methods are not effective in the task of imbalanced image classification for ignoring the distribution bias. In this study, we propose a Balanced Active Learning (BAL) method for imbalanced image classification. BAL estimates the probability of a sample belonging to minority or majority classes and compensates the annotation query for the minority classes, thus alleviating the class imbalance in the selected training samples. Experiments on three imbalanced image classification datasets, imbalanced CIFAR-10, ISIC2020, and Caltech256, showed that BAL achieved new state-of-the-art performance of active learning in a variety of classification tasks and different types of imbalance.

Class specific biased extrapolation of images in latent space for imbalanced image classification

In this work, we study the effectiveness of prior re-sampling approaches for imbalanced image classification. We propose to investigate inter-class and within-class characteristics and conduct class specific extrapolation re-sampling for optimal imbalanced learning.

Imbalanced image classification with complement cross entropy

Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions still limit the wide applicability of these models due to degradation in performance. To solve this problem, in this paper, we concentrate on the study of cross entropy which mostly ignores output scores on incorrect classes. This work discovers that neutralizing predicted probabilities on incorrect classes improves the prediction accuracy for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy based on this finding. The proposed loss makes the ground truth class overwhelm the other classes in terms of softmax probability, by neutralizing probabilities of incorrect classes, without additional training procedures. Along with it, this loss facilitates the models to learn key information especially from samples on minority classes. It ensures more accurate and robust classification results on imbalanced distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of the proposed method compared to other state-of-the-art methods.

Multi-class imbalanced image classification using conditioned GANs

Multi-class imbalanced image classification using conditioned GANs

An Imbalanced Image Classification Method for the Cell Cycle Phase

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

Deep Attention-Based Imbalanced Image Classification.

Class imbalance is a common problem in real-world image classification problems, some classes are with abundant data, and the other classes are not. In this case, the representations of classifiers are likely to be biased toward the majority classes and it is challenging to learn proper features, leading to unpromising performance. To eliminate this biased feature representation, many algorithm-level methods learn to pay more attention to the minority classes explicitly according to the prior knowledge of the data distribution. In this article, an attention-based approach called deep attention-based imbalanced image classification (DAIIC) is proposed to automatically pay more attention to the minority classes in a data-driven manner. In the proposed method, an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to the minority classes, as possible into the discriminative feature learning process by assigning the attention for different classes jointly in both the prediction and feature spaces. With the proposed object function, DAIIC can automatically learn the misclassification costs for different classes. Then, the learned misclassification costs can be used to guide the training process to learn more discriminative features using the designed attention networks. Furthermore, the proposed method is applicable to various types of networks and data sets. Experimental results on both single-label and multilabel imbalanced image classification data sets show that the proposed method has good generalizability and outperforms several state-of-the-art methods for imbalanced image classification.

Imbalanced Breast Cancer Classification Using Transfer Learning

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e., the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.

Towards Imbalanced Image Classification: A Generative Adversarial Network Ensemble Learning Method

Learning from minority class has been a significant and challenging task which has many potential applications. Weather classification is such a case of imbalanced label distribution. This is because in places like Beijing, some types of weather, such as rain and snow, are relatively rare compared to sunny and haze days. Existing methods are primary to classify the weather conditions relying on expensive sensors or human assistance, which however usually are expensive and time-consuming. In this paper, we propose a new ensemble framework based on the advanced generative adversarial network and an effective data cleaning way to address the class imbalance problem for weather classification. The proposed method not only generates new and reliable samples for the minority class to restore balance, but also filters those generated samples which are unreliable. Experiments show that our approach outperforms the state-of-the-art methods by a huge margin for imbalanced weather classification on several benchmark data sets.

KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling

Imbalanced learning has become a research emphasis in recent years because of the growing number of class-imbalance classification problems in real applications. It is particularly challenging when the imbalanced rate is very high. Sampling, including under-sampling and over-sampling, is an intuitive and popular way in dealing with class-imbalance problems, which tries to regroup the original dataset and is also proved to be efficient. The main deficiency is that under-sampling methods usually ignore many majority class examples while over-sampling methods may easily cause over-fitting problem. In this paper, we propose a new algorithm dubbed KA-Ensemble ensembling under-sampling and over-sampling to overcome this issue. Our KA-Ensemble explores EasyEnsemble framework by under-sampling the majority class randomly and over-sampling the minority class via kernel based adaptive synthetic (Kernel-ADASYN) at meanwhile, yielding a group of balanced datasets to train corresponding classifiers separately, and the final result will be voted by all these trained classifiers. Through combining under-sampling and over-sampling in this way, KA-Ensemble is good at solving class-imbalance problems with large imbalanced rate. We evaluated our proposed method with state-of-the-art sampling methods on 9 image classification datasets with different imbalanced rates ranging from less than 2 to more than 15, and the experimental results show that our KA-Ensemble performs better in terms of accuracy (ACC), F-Measure, G-Mean, and area under curve (AUC). Moreover, it can be used in both dichotomy and multi-classification on both image classification and other class-imbalance problems.