Automatic Detection and Classification of Lung Nodules in CT Images

Abstract

Lung Cancer is one of the most dangerous threats for human life since many decades. When compared to other types of cancers, Lung cancer is very prevalent with less survival rate. Computed-Tomography-images are the main source of lung nodule detection as it provides detailed information about the nodules. Though there are many advanced techniques available in the present scenario like CNN, deep Neural Networks etc. For training purposes, all these existing methods use raw chest CT images which contain more complicated information like Blood vessels, muscles, lymph nodes, air ways, bones etc. Hence if the nodule is segmented effectively and that in turn used to train the system to classify cancerous and non-cancerous, then the results will be extraordinarily efficient. This paper presents the simple and efficient segmentation of Lung nodules and automatic classification of Nodules into Benign or Malignant using novel algorithms. The techniques used includes thresholding, morphological operations, pixels closures and mathematical model, for effective detection and classification of Nodules. The proposed algorithm is evaluated on axial plane CT Scans from LIDC-IDRI database, and the comparative study proved the effectiveness of segmentation and classification. The experimental result achieved the specificity of 98.67%, Accuracy of 97.98% and F-score of 1.0859 for β = 0.5.