Abstract
The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.
Highlights
Lung cancer has a high causality rate
Lung are segmented from the Digital Imaging and Communications in Medicine (DICOM) Computed Tomography (CT) scans, and in the second phase nodules are detected from the lungs
The results reveal that the proposed method is reliable for lung nodule detection
Summary
Lung cancer has a high causality rate. According to a survey [1], more than 1.37 million people died from Lung cancer throughout the world only in 2008. 1.74 million new cancer cases and 0.61 million cancer deaths in the year 2018 [2]. Two main reasons for the high mortality rate in lung cancer are the delay in early diagnosis and the poor prognosis [3]. The study reveals that 70% of lung cancers are diagnosed in too advanced stages, where the cancer prognosis is ineffective. Early diagnosis of cancer is momentous for increasing the patient’s chances of survival
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