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Many blue and white porcelain are unearthed in Jingdezhen every year. The patterns on the sherds have important research significance. At present, the classification of porcelain shards is mainly based on manual work, which has the disadvantages of large workload. The use of automatic classification methods also faces complex patterns and sample sizes. In order to solve these problems, this paper proposes a new automatic recognition method based on deep learning, including data preprocessing method combined with color segmentation algorithm, a new data augmentation method FCutMix for regions of interest, a new integration strategy and the redesigned deep network model FFCNet that integrates multiple features. After experiments, the data preprocessing method, feature fusion method and integration strategy proposed in the paper can effectively improve the performance of the model by removing redundant information and adding effective features. The FCutMix method can also obtain more accurate mixed samples than the traditional CutMix. The method proposed in this paper improves the accuracy of tasks in 14 categories from 71.7% to 83.2% in a dataset containing only 373 images of porcelain sherds. In the future, this research will further design the network structure and multi-level feature fusion. [ABSTRACT FROM AUTHOR]

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images. [ABSTRACT FROM AUTHOR]

Gastric cancer is a deadly disease which should be treated in time, in order to increase the life span of the patient. Computer aided diagnosis will help the doctors to identify the gastric cancer easily. In this paper, a CAD based approach is projected to discriminate and categorize gastric cancers from various other intestinal disorders. The approach provided the Xception network, with individual convolutions. The projected technique applied three procedures: Google's Auto Augment for augmentation purpose, BCGDU-Net for segmentation and Xception network for lesion classification. The augmentation and segmentation facilitated theclassifying technique to be enhanced because this methodology prohibited overfitting. The segmented region is classified as cancerous or non-cancerous based on the features extracted in the Xception network training phase. This method is analyzed with the different combinations of augmentation, segmentation with and without ROC. It is found that the area under ROC curve for augmentation and segmentation is higher than the other two cases. Moreover, this technique provides a segmentation accuracy of 98% when compared with existing methods like fuzzy C means, global thresholding, BCD-Net, U Net. The classification accuracy of 98.9% is obtained, which is higher than the existing techniques like Res Net, VGG net, Mobile Net. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel deep learning model for reliable quantification of dentinal tubule occlusions instead of manual assessment techniques, and the performance of the model was compared to other methods in the literature. Ninetysix dentin samples were cut and prepared with desensitizing agents to occlude dentinal tubules on different levels. After obtaining images via scanning electron microscope (SEM), 2793 single dentinal tubule images with 48 x 48 resolution were segmented and labeled. Data augmentation techniques were applied for improvement in the learning rate. The augmented data having a total of 10700 images belonging to five classes were used as the network training dataset. The proposed convolutional neural network (CNN) is a class of deep learning model and was able to classify the degree of dentinal tubule occlusions into five classes with an overall accuracy rate of 90.24%. This paper primarily focuses on developing a CNN architecture for detecting the level of dentin tubule occlusions imaged by SEM. The results showed that the proposed CNN architecture is an immensely successful alternative and allowed for objective and automatic classification of segmented dentinal tubule images. [ABSTRACT FROM AUTHOR]

Mango (Mangifera Indica L. Family Anacardiaceae) is a climatic fruit with a short shelf life. A significant percentage of fruit is wasted each year due to the time-consuming manual grading and classification process. There is a need to replace the traditional methods by adopting automation technologies in the agriculture sector. This paper presents a deep learning-based approach for automated classification and grading of eight cultivars of harvested mangoes based on quality features such as color, size, shape, and texture. Five types of data augmentation methods were used: images rotation, translation, zooming, shearing, and horizontal flip. We compared three architectures of 3-layer Convolutional Neural Network (CNN): VGG16, ResNet152, and Inception v3 on augmented data. The proposed approach achieved up to 99.2% classification accuracy and 96.7% grading accuracy respectively using the Inception v3 architecture of CNN. [ABSTRACT FROM AUTHOR]

Pest recognition in the agriculture field plays a critical issue for the farmers which diminishes economic growth. So far, traditional practices were followed by farmers to increase yield production. Nowadays, researchers execute a deep learning approach to classify various kinds of images practically. In this paper, Deep convolutional neural networks (DCNN) are used to recognise ten kinds of pests present in the paddy crop. The data repository contains around 3549 pest images that affect the paddy crops, Since Deep Learning supports well for larger data-set so the data augmentation process is carried out. The neural model is build using various kinds of DCNN architecture, interpretation was made over the models based on the accuracy rate and the performance. The transfer learning approach is applied over the pest data set by fine-tuning the hyperparameters and the layers of the ResNet-50 model. By comparing the resultant value, the fine-tuned ResNet-50 model produced better accuracy of 95.012% among other models. The obtained resultant value describes the effective performance of the model in pest disease classification. [ABSTRACT FROM AUTHOR]

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children's Medical Center pneumonia dataset. [ABSTRACT FROM AUTHOR]

In this paper, we examine a novel data augmentation (DA) method that transforms an image into a new image containing multiple rotated copies of the original image. The DA method creates a grid of cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. We investigate the use of deep learning to assess the classification performance on the rotation matrix or original dataset with colour constancy versions of the datasets. For the colour constancy methods, we use two well-known retinex techniques: the multi-scale retinex and the multi-scale retinex with colour restoration for enhancing both original (ORIG) and rotation matrix (ROT) images. We perform experiments on three datasets containing images of animals, from which the first dataset is collected by us and contains aerial images of cows or non-cow backgrounds. To classify the Aerial UAV images, we use a convolutional neural network (CNN) architecture and compare two loss functions (hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbour classifier or a support vector machine. The best approach is then used to examine the colour constancy DA variants, ORIG and ROT-DA alone for three datasets (Aerial UAV, Bird-600 and Croatia fish). The results show that the rotation matrix data augmentation is very helpful for the Aerial UAV dataset. Furthermore, the colour constancy data augmentation is helpful for the Bird-600 dataset. Finally, the results show that the fine-tuned CNNs significantly outperform the CNNs trained from scratch on the Croatia fish and the Bird-600 datasets, and obtain very high accuracies on the Aerial UAV and Bird-600 datasets. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) seeks to identify the same individual across different cameras by matching their corresponding images. The current ReID datasets are limited in size and diversity, especially in terms of clothing changes, making traditional techniques vulnerable to appearance variations. Further, current approaches rely heavily on appearance features for discrimination, which is unreliable when a person's appearance changes. We hypothesize that the ReID accuracy can be enhanced by training the ReID model on a large volume of diversified training data and combining multiple features for discrimination. We use the image channel shuffling data augmentation method, producing a large volume of diversified training data. Also, a two-stream visual and spatial-temporal method is proposed to learn the feasible features for appearance change scenarios. The appearance features obtained from the visual stream are combined with spatiotemporal information to discriminate between two people. The proposed approach is evaluated for its robustness on short-term and on long-term datasets. The presented two-stream approach outperforms earlier methods by achieving Rank1 accuracy of 98.6% on Market1501, 95.52% on DukeMTMC-reID, 76.21% on LTCC, and 91.76% on VC-Clothes, respectively. [ABSTRACT FROM AUTHOR]