Research on Image Processing Technology Based on Artificial Intelligence and Visual Algorithms

Deep Learning (DL), a branch of Artificial Intelligence (AI), is extensively used in Computer Vision (CV) technology, particularly in medical image analysis. The DL approaches have achieved notable success in the classification of medical images. However, the existing AI algorithms have limitations such as poor detection, misclassification, time complexity, and so on. To overcome the problems, a Spatio-Temporal Graph Convolutional Network (ST-GCN) is proposed to classify medical images correctly by learning from relevant features. The image processing techniques such as preprocessing, segmentation, feature extraction, and classification for medical images are described in this research. The presented ST-GCN model achieved a high accuracy of 96.3%, recall of 96.1%, precision of 96.4%, and F1-score of 96.2% which is high when compared with previous AI and computer vision algorithms such as Convolutional Neural Network (CNN, Deep Neural Networks (DNN), Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Bidirectional –LSTM (BiLSTM), Gated Recurrent Unit (GRU).