Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method

This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.