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Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An accurate and rapid identification of species is one of the most important parts of freshwater fish pre-treatment. However, deep learning-based freshwater fish classification cannot quantitatively describe the feature parameters of fish, due to the complicated feature extraction. This study aims to propose a deep learning-based fish feature point detection and freshwater fish classification. Firstly, an image acquisition device was built to acquire the images of five freshwater fish species: bream, bighead carp, grass carp, silver carp, and common carp. Secondly, the data enhancement was performed on the original images. LabelMe software was also used to label 20 feature points of the fish body, in order to construct a freshwater fish feature point detection dataset. The feature point of freshwater fish was detected using an improved AlexNet feature point detection model with the convolutional neural networks (CNN). The improved AlexNet model also adjusted the network structure of the traditional model, in order to accommodate the feature point detection task by the modified convolutional kernel size, the removal of the local response normalization layer, the addition of batch normalization layer, and the replacement of the loss function. The Euclidean distance between feature points was extracted as the feature value using the coordinates of the feature points. Feature vectors were constructed. Fisher discriminant analysis was used to identify the species of freshwater fish. A series of ablation experiments were conducted on the improved AlexNet model to clarify the impact of different improvement schemes on model performance. The results showed that the improved schemes were beneficial to improve the training efficiency and performance of the model. The average value of normalized mean error (NME) of the improved AlexNet model on the test set was 0.0099, the failure rate (FR) at thresholds of 0.02 and 0.03 were 2.50% and 0.83%, and the average detection time was 0.037 s, indicating the better accuracy and error distribution. The number of parameters and floating-point operations per second (FLOPs) of the improved AlexNet model were smaller than those of the VGG16 model. The test set detection was also better than that of the VGG16 model, compared with different feature extraction models. Although the number of parameters and FLOPs of the improved AlexNet model was larger, the average NME and the FR were lower than that of the MobileNetV2 model. The recognition accuracy of 98.0% was achieved in the freshwater fish species recognition using the model and Fisher discriminant analysis for five freshwater fish species. The average recognition time of a single image was 0.368s. The feature point detection model can be expected to detect the feature points of five species of freshwater fish with high accuracy. The finding can also facilitate freshwater fish species identification, size detection, and fish segmentation. The freshwater fish species identification using feature point detection and Fisher's discriminant analysis can lay the foundation for the development of an automated freshwater fish classification device. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For improving the classification accuracy of high-resolution remote sensing imagery, an accurate and automatic land cover classification convolutional neural network (LCC-CNN) for high resolution remote sensing imagery based on deep learning is proposed. The training dataset is constructed by GF-2 and BJ-2 image. The LCCCNN is designed with different sizes of dilated convolution in extracting multiscale features from remote sensing imagery. A novel loss function is designed for distinguishing the fuzzy boundary among different land cover classification regions. With the structure of encoder-decoder, LCC-CNN export pixel-level classification result, the experiments show that the overall classification accuracy, IOU, and Kappa coefficient of LCC-CNN are 87. 17%, 0. 773 2, and 0. 829 1. Compared with the existing approaches, such as SVM and decision tree, the accuracy, IOU, and Kappa coefficient are improved by 8%, 10%, and 11%. The experiments also prove that LCC-CNN has strong applicability for high-resolution image classification without designing classification features and parameters. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The difficulties of clinical diagnosis and treatment in neurosurgery were introduced in brief. The application and important meaning of the deep learning technology, multimodal fusion technology and image guidance techniques in the diagnosis and treatment of neurosurgery were elaborated. In addition, the development and challenges of precise diagnosis and treatment in neurosurgery were discussed. It's pointed out continuous progress of intelligent analysis of medical images would help achieve accurate diagnosis and treatment for neurosurgery, thus reduce the occurrence of misdiagnosis and improve the success rate of operation. Furthermore, it would promote the intelligent development of clinical diagnosis and treatment in neurosurgery. [ABSTRACT FROM AUTHOR]