CNN

The COVID-19 showed us that rapid and accurate diagnostics is a necessity.  Therefore, researchers began to implement deep learning models that can help the doctors to reach faster and reliable results, but there are more development to be done.  In our research paper, we introduced an innovative approach to enhance the Vision Graph model's accuracy for better results.  Our method exploits the strength of the ConvMixer architecture and Attention mechanism.  We start by utilizing Depthwise convolution and Pointwise convolution to capture spatial information in detail whi

A modified neural network model based on Yolo V5 was developed and the quality metrics of object classification on video images built on the basis of existing known basic neural network architectures were compared. The application of convolutional neural networks for processing images from video surveillance cameras is considered in order to develop an optimized algorithm for detecting and classifying objects on video images. The existing models and architectures of neural networks for image analysis were analyzed and compared.

The article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated.