Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification Article Swipe
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3281546
· OA: W4379742977
One of the most complex areas of image processing is image classification, which is heavily \nrelied upon in clinical care and educational activities. However, conventional models have reached their \nlimits in effectiveness and require extensive time and effort to extract and choose classification variables. \nIn addition, the large volume of medical image data being produced makes manual procedures ineffective \nand prone to errors. Deep learning has shown promise for many classification problems. In this study, a deep \nlearning-based classification model is developed to decrease misclassifications and handle large amounts of \ndata. The Adaptive Guided Bilateral Filter is used to filter images, and texture and edge attributes are gathered \nusing the Spectral Gabor Wavelet Transform. The Black Widow Optimization method is used to choose the \nbest features, which are then input into the Red Deer Optimization-enhanced Gated Deep Reinforcement \nLearning network model for classification. The brain tumor MRI dataset was used to test the model on the \nMATLAB platform, and the results showed an accuracy of 98.8%.
