Osteoporosis Analysis using Evolutionary Algorithm

Already, a greater number of techniques and algorithms are evolving. but the existing system was lacking somewhere and had limitations in its implementation. At present, existing studies or research are done only for local optima. Local optima were the main problem in our study. The objective of the system revolves around the combination of natural and fused evolutionary algorithms with clinical expertise data to result in global optimal early detection and better accuracy of osteoporosis to get the best fitness score for the improvement of patient. We are collecting the sample data and diagnosing the patient with X-ray techniques. The X-ray image will be in noisy form. To eliminate noise in the image, we are doing the denoising and deblurring processes. After that, enhance the image, and pixel-based segmentation is done in this x-ray image. From the sample data, we are extracting features. . So, we can obtain the result from the performance parameters, i.e., whether the patient has osteoporosis or not. Through this process, we get the 97.93 best accuracy compared to other studies.