Imbalanced image classification is one of the most important and difficult issues in data mining. With the inability of standard classification algorithms, Capsule neural networks (CapsNet) provide a good platform for designing imbalanced classification models by considering spatial communication of features, compared to other deep networks such as Convolutional Neural Networks (CNN). On the other hand, crack bifurcation in the surface cracks is one of the anomalies and minority categories in concrete structures that can be effective in the maintenance of concrete structures and cost management. Also, the surface crack image sets are suitable data for evaluating imbalanced classification due to their characteristics. Therefore, in this paper, a new architecture based on CapsNet is introduced to evaluate the imbalanced classification of surface crack images in the concrete structures. Examination and comparison of the proposed network with CNN in balanced and imbalanced image classification of surface cracks on 13,500 sets of collected images showed the superiority of the proposed network. Also, the proposed network showed a significant advantage compared to CNN in investigating the effect of reducing the number of training images on classification accuracy. This network performed balanced classification of surface cracks with 99.56% accuracy. Also, the proposed network has an accuracy of 80% up to the imbalance of theminority group to the 1:8 minority, which is very suitable compared to CNN.

With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure. With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure.In order to improve the classification accuracy, the feature extraction approach through the designed network and the classification by the Extreme Gradient Boosting was compared with the classification method by the global deep network. The proposed capsule approach consists of 3 basic layers: 1) Prime caps, which are capsules of size 8 and 32 with 9 × 9 filters and movement step 2, 2) Digitcaps with 10 16-dimensional capsules, and 3) fully connected layer. The results of examining two approaches for deep networking as well as combining capsule networks with XGBoost reinforcement tree algorithm were compared. Approaches such as SVM, RF-200, LSTM, GRU and GRU-Pretanh were considered to compare the proposed approach based on the configurations mentioned in their research.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined. The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined.The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.

Effective breast cancer screening is essential for early detection and treatment. Ultrasound (US) radio frequency (RF) data offers a novel, equipment-independent approach. However, class imbalance and limited interpretability hinder its application in clinical practice. This study proposes a hybrid deep learning model combining a pre-trained convolutional neural network (CNN) based on VGG16 and capsule neural networks (CapsNets) to classify breast lesions. The model was evaluated using an RFTSBU dataset, comprising 220 data points from 118 patients, acquired on the SuperSonic Imagine Aixplorer® system with a linear transducer. To address data imbalance, the synthetic minority over-sampling technique (SMOTE) was employed to generate synthetic samples while preserving data distribution. Furthermore, Gaussian process (GP) was applied to fine-tune CapsNet hyperparameters, improving classification performance. Three experiments were conducted to classify breast lesions into two, three, and four classes: (I) CapsNet with balanced datasets based on class weight, (II) CapsNet with balanced datasets using SMOTE, and (III) CapsNet with hyperparameters optimized using GP on SMOTE-balanced datasets. The proposed model achieved average accuracies of 98.81%, 97.89%, and 95.94% for two-, three-, and four-class classifications, respectively. The hybrid VGG16-CapsNet model effectively addresses class imbalance and captures critical lesion attributes such as size, perspective, and orientation. Integrating GP optimization achieves superior accuracy in multi-class breast lesion classification. The proposed approach can serve as a valuable aid in breast tumor classification using US RF B-mode images. Its enhanced interpretability and efficiency enable clinicians to move beyond binary classification, facilitating identifying and differentiating a broader spectrum of breast lesions.

Male infertility as an effective factor can affect the lives of infertile couples. Sperm morphology is an important step in evaluating and examining semen in male infertility. The lack of samples of sperm head abnormalities compared to natural sperm samples can make the classification of sperm head images into an imbalanced classification problem. With the inability of common classification algorithms, capsule neural networks (CapsNet) provide a suitable platform for designing imbalanced classification models compared to other deep networks. Also, Generative Adversarial Networks (GANs) help improve the imbalanced classification of images by producing appropriate artificial samples. To this end, in this paper a new architecture is introduced based on CapsNet and GAN to evaluate the imbalanced classification of human sperm images. Reviewing and comparing the proposed model with other deep learning models in the balanced and imbalanced classification of human sperm images showed the superiority of the proposed model. Investigating the general methods of increasing data with the proposed model to increase data, it was concluded that the general methods have less resistance to reducing the number of data than the proposed model. Balanced classification of human sperm images was done by proposed model with 98.1 % accuracy. The proposed model also maintained a high sensitivity to the minority to the majority of 1:25, indicating its proper performance in the imbalanced classification of sperm images.