Abstract
Medical image denoising is an essential pre-processing step in medical image processing which improves the performance of clinical diagnosis and prognosis. The high level medical image processing algorithms like segmentation, classification etc. works better if the image is denoised appropriately. The main objective of this research work is to find and replace only the corrupted pixels with suitable estimates of pixels in medical images. The other pixels which are not corrupted are left undisturbed, thereby preserving the image quality for proper diagnosis. For the primary task of finding the corrupted pixels, an ensemble of machine learning (EML) classifiers namely Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT or RT) and Random Forest (RF) are used by supervised learning methods. The final classification output is determined by the majority voting of the outputs of each ML classifier which works in parallel. By adopting this method, a classification accuracy of 99.87% is achieved.
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Kumarasamy, K., Maria Wenisch, S., Balaji, S., Jenifer Suriya, L.J., Jerlin, A., Robert Rajkumar, S. (2021). Improving Impulse Noise Classification Using Ensemble Learning Methods. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_16
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