A random forest for lung nodule identification

Lee, S.L.A.; Kouzani, Abbas Z.; Hu, Eric

doi:10.1109/tencon.2008.4766750

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Lee et.al. (2008) [11] projected a random forest tree based classifier to discover all the nodules in the images and detailed a low false detection rate. The projected process comprises three stages namely image acquirement, removal of background and detection of lung nodules.…”

Section: Review Of Literaturementioning

confidence: 99%

Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

Perumal¹,

Velmurugan²

2018

IJET

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In recent years, prediction of cancer at earlier stages is obligatory to increase the chance of survival of the afflicted. The most dreadful type is lung cancer, which is identified as one of the most common diseases among humans worldwide. In this research work, the raw input image which usually suffers from noise issues are highly enhanced using Gabor filter image processing. The region of interest from lung cancer images are extracted with Otsu’s threshold segmentation method and 5- level HAAR discrete wavelet transform method which possess maximum speed and high accuracy. The proposed Enhanced Artificial Bee Colony Optimization (EABC) is applied to detect the cancer suspected area in CT (Computed tomography) scan images. The proposed EABC implementation part, utilizes CT (Computed Tomography) scanned lung images with MATLAB software environment. This method can assist radiologists and medicinal experts to recognize the illness of syndromes at primary stages and to evade severe advance stages of cancer.

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Section: Review Of Literaturementioning

confidence: 99%