1. A non invasive computer aided diagnosis system for early detection of lung carcinoma in ct medical images
Authors : V.i.mebin Jose, Dr.t.arumuga Maria Devi
Pages : 125-130
DOI : http://dx.doi.org/10.21172/1.841.22
Keywords : Enhanced and Hybrid based Feature Detector, Image Enhancement Factor, Local binary Pattern, stationary wavelet transform Abstract :Lung cancer is a potentially fatal disease caused mainly by environmental factors that mutate genes encoding critical cell regularities proteins. This paper investigates the early detection of lung cancer to improve the performance of computer aided diagnosis system. The Existing multimodal sparse representation based classification cannot produce the optimal detection rate due to the in consistency of feature selection. Moreover, the computation time is also high. To overcome these limitations, a novel cancer detector developed with higher detection rate with minimal computation time is called EHFD. The proposed (EHFD) Enhanced and Hybrid Feature based Detector outperforms well in real time because of combining optimal features from enhanced and non-enhanced images. The experimental results validated that the proposed method improved the IEF by 19% and detection overshoot by 93% compared with existing methods. Therefore, the proposed method is most suitable for early estimation and diagnosis of lung cancer pretty well. The produced results demonstrate that the detection performance fits for diagnosis of lung cancer in early stage and integrate with the current diagnosis system.
Citing this Journal Article :V.i.mebin Jose, Dr.t.arumuga Maria Devi, "A non invasive computer aided diagnosis system for early detection of lung carcinoma in ct medical images ", Volume 8 Issue 4-1 - August 2017, 125-130
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