Analysis of Tuberculosis (TB) on X-ray Image Using SURF Feature Extraction and the K-Nearest Neighbor (KNN) Classification Method
| Autor: | Reyhan Achmad Rizal, Nur Azizah, Nurlela Octavia Purba, Lidya Aprilla Siregar, Kristina P. Sinaga |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Communication. Mass media Feature extraction General Engineering Pattern recognition Information technology T58.5-58.64 P87-96 Field (computer science) k-nearest neighbors algorithm ComputingMethodologies_PATTERNRECOGNITION X ray image General Earth and Planetary Sciences Classification methods Artificial intelligence tuberkulosis knn surf business Supervised training General Environmental Science |
| Zdroj: | JAICT (Journal of Applied Information and Communication Technologies), Vol 5, Iss 2, Pp 9-12 (2020) |
| ISSN: | 2541-6359 2541-6340 |
| DOI: | 10.32497/jaict.v5i2.1979 |
| Popis: | With current technological developments, machine learning has become one of the most popular methods, one of the popular machine learning algorithms is k-nearest neighbors (KNN). Machine learning has been widely used in the medical field to analyze medical datasets, in this study the k-nearest neighbors (KNN) machine learning algorithm will be used because of its good level of accuracy in recognition and is included in the supervised learning algorithm group. The results showed the k-nearest neighbors (KNN) method in recognizing x-ray images of tuberculosis (TB) using SURF feature extraction with an average accuracy of 73%. |
| Databáze: | OpenAIRE |
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