Abstract
Tuberculosis is an infection that influences numerous individuals worldwide. While treatment is conceivable, it requires an exact conclusion first. Especially in developing countries there are by and large accessible X-beam machines, yet frequently the radiological aptitude is missing for precisely surveying the pictures. An automated vision based framework that could play out this undertaking rapidly and inexpensively could radically improve the capacity to analyze and at last treat the sickness. In this paper we propose image analysis based framework using various machine learning techniques like SVM, kNN, Random Forest and Neural Network for effective identification of tuberculosis. The proposed framework using neural network was able to classify better than other classifiers to detect Tuberculosis and achieves accuracy of 80.45%.
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Ghanshala, T., Tripathi, V., Pant, B. (2020). An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_4
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DOI: https://doi.org/10.1007/978-981-15-6634-9_4
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