Main Article Content
The great demand for Magnetic Resonance Imaging (MRI) in the world of medical field has helped the doctors to analyse and detect relevant disease. MRI is not only an effective technique for the assessment of pathology but also a valuable method for tracking the progression of disease. The ability of MRI to provide rapid 3D visualization has led to extensive use of this technology in the diagnosis and therapy of pathologies of different organs that are then used during treatment. Deep learning algorithm for the initial, intermediate and final stages of knee pain is developed. Clustering technique is commonly used in image segmentation in order for users to access a data image to distinguish common preferences and patterns. Clustering is intended to divide a dataset into groups or clusters where similarities between the clusters are minimized while similarities within the clusters are maximized. This work focuses on a convolutionary neural network (CNN) for classification of knee pain and popular K-mean clusters to segment the image. Results of both techniques are presented and accuracy is calculated..
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.