A biologically inspired visual integrated model for image classification

Style classification of media painting images by integrating ResNet and attention mechanism

The progress of deep learning technology has made image classification an important application field. Image style classification is a complex task involving the recognition of the whole picture, including the recognition of salient features and detailed features. This study is based on the ResNet algorithm and has improved its Resnet 50 version with excellent performance. In the model architecture, we introduce blur pool operation and replace the traditional Relu function with Celu activation function. In addition, the triplet attention mechanism was integrated to further enhance the model performance. Through a series of experiments, it is found that the improved ResNet50 model has the highest classification accuracy of 80.6% on large-scale image data sets, which is 11.7% higher than the traditional ResNet50 model. In terms of recognition of similar style images, the model incorporating triplet attention demonstrated higher average accuracy (74%) and recall (82%). This improvement has achieved certain results and has certain technical reference value for various styles of image classification fields.

Pavement Disease Detection through Improved YOLOv5s Neural Network

An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (floating-point operations) in the feature channel fusion process. Mosaic data augmentation is also added to improve the feature expression performance. A public road disease dataset is reconstructed to verify the performance of the proposed method. The proposed model is trained and deployed to NVIDIA Jetson Nano for the experiment, and the results show that the average accuracy of the proposed model reaches 88.17%, increased by 4.01%, and the model FPS (frames per second) reaches 12.51, increased by 184% compared with the existing YOLOv5s. Case studies show that the proposed method satisfies the practical application requirements of pavement disease detection.

Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity

BACKGROUND AND OBJECTIVES

Diabetic retinopathy is a type of diabetes that causes vascular changes that can lead to blindness. The ravages of this disease cannot be reversed, so early detection is essential. This work presents an automated method for early detection of this disease using fundus colored images.

METHODS

A bio-inspired approach is proposed on synaptic metaplasticity in convolutional neural networks. This biological phenomenon is known to directly interfere in both learning and memory by reinforcing less common occurrences during the learning process. Synaptic metaplasticity has been included in the backpropagation stage of a convolution operation for every convolutional layer.

RESULTS

The proposed method has been evaluated by using a public small diabetic retinopathy dataset from Kaggle with four award-winning convolutional neural network architectures. Results show that convolutional neural network architectures including synaptic metaplasticity improve both learning rate and accuracy. Furthermore, obtained results outperform other methods in current literature, even using smaller datasets for training. Best results have been obtained for the InceptionV3 architecture with synaptic metaplasticity with a 95.56% accuracy, 94.24% F1-score, 98.9% precision and 90% recall, using 3662 images for training.

CONCLUSIONS

Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, training simplicity and high performance.