Author:
Puthuru Kavya ,Kouluri Sreeja Reddy ,Tholla Ujwala ,Amuru Mounitha ,Ms. Rekha M S
Abstract
Osteoarthritis (OA) of the knee is a common degenerative joint disease that is characterized by inflammation and cartilage degradation, which results in pain and impairment. For prompt intervention and care, early detection and precise assessment of the severity of OA are essential. In this research, we offer a unique method for automated knee OA diagnosis and severity assessment using medical imaging data, especially X-ray images, using convolutional neural networks (CNNs). Our CNN architecture is intended to identify complex features from knee X-rays and categorize them into various OA severity levels, from moderate to severe. A sizable dataset of knee X-ray pictures with accompanying OA severity scores is used by the suggested model. Our method shows promise in correctly detecting knee OA after thorough testing and validation on a variety of datasets.
Reference34 articles.
1. Ashok Kumar D., UmmalSariba Begum T., “A Novel plan ofElectronic Voting Framework Utilizing Fingerprint”, Universal Diary of Imaginative Innovation & Imaginative Building (ISSN:2045- 8711),Vol.1,No.1. pp: 12 19, January 2011.
2. Benjamin B., Bederson, Bongshin Lee., Robert M. Sherman., Paul S., Herrnson, Richard G. Niemi., “Electronic Voting Framework Ease of use Issues”, In Procedures of the SIGCHI conference on Human components in computing frameworks, 2003.
3. California Web Voting Errand Drive. “A Report on the Possibility of Web Voting”, Jan.2000.
4. Chaum D., “Secret-ballot receipts: Genuine voter-verifiable elections”, IEEE Security and Security, 2(1):38-47, 2004.
5. Darcy, R., & McAllister, I., “Ballot Position Effects”, Constituent Considers, 9(1), pp.5-17, 1990.