FuzzyNet: Medical Image Classification based on GLCM Texture Feature

In recent study has found that high-precision monitoring, big data, and medical diagnosis continue to be hampered by transit delays. We designed and implemented an unique fuzzy logic-based method to help with this problem. Currently, a high-accuracy automated approach for detecting anomalies in X-ray images is being developed. Pre-processing image technologies are used to improve the quality of medical pictures in order to achieve high accuracy while utilizing a small number of system resources. Noise reduction and contrast enhancement are two procedures engaged in image pre-processing that assist to the delivery of a rapid anomaly detecting system. We suggested a Fuzzy Net model Classification Based on GLCM Feature and particle swam optimization technique in this study. This method categorizes the X-ray pictures. The musculoskeletal radiographs (MURA) dataset is classified as normal or abnormal. We also examined at performance indicators and loss over epochs, which we compared using a confusion matrix and displayed plots of the model’s learned membership functions.