Lung nodule type classification in CT images using UNet based segmentation and ANFIS based classification

Abstract

The Early detection and classi?cation of lung nodules using computer-aided diagnosis (CAD) is very much needed to reduce mortality rates in lung cancer patients. The spontaneous and accurate classification of lung nodule type is very supportive for the precise lung cancer diagnosis. In this paper, a novel approach is proposed which employs deep learning for segmentation and m-ANFIS (modified Adaptive Neuro Fuzzy Inference System) for lung nodule type classification using CT (Computed Tomography) images. The proposed method uses UNet architecture for the effective ROI segmentation on the LIDC (Lung Image Database Consortium) database images. The Texture, statistical and shape features of the segmented region is extracted using GLCM (Gray-level Co-occurrence Matrix) algorithm. Then ANFIS classifier is employed to classify the nodules into various types such as juxta-pleural, juxta-vascular, well-circumscribed, pleural-tail and ground-glass opacity. The proposed method is executed in Python platform and performance of the system is evaluated. The results show that the proposed method achieves sensitivity, accuracy, precision, specificity and AUC (Area under ROC Curve) of 97.18%, 98.87 %, 98.72%, 98.3% and 0.978 respectively under epoch 30. These results are compared with other lung nodule type classification approaches and found to perform better than other methods.

DOI: 10.61416/ceai.v25i4.8462