Small lung nodules detection based on local variance analysis and probabilistic neural network

Computer Methods and Programs in Biomedicine
Marcin WoźniakKatarzyna Frankiewicz

Abstract

In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis. In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier. The performance of our approach is 92% of corre...Continue Reading

Citations

Mar 14, 2020·Sensors·Sultan AlmotairiMohammed A-M Salem
Oct 6, 2020·Health Information Science and Systems·Aras M Ismael, Abdulkadir Şengür
Aug 3, 2021·Ingénierie Et Recherche Biomédicale : IRBM = Biomedical Engineering and Research·R KarthikG S Kathiresan

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