Lung cancer computed tomography image classification using Attention based Capsule Network with dispersed dynamic routing

• Published in: Expert systems 2024-09, Vol.41 (9), p.n/a
• Main Authors: Paramasivam, Ramya, Patil, Sujata N., Konda, Srinivas, Hemalatha, K. L.
• Format: Article
• Language: English
• Subjects: Attention based Capsule Network; Classification; Computed tomography; Datasets; Diagnosis; dispersed dynamic routing; Generative Adversarial Network; Generative adversarial networks; Gray Level Co‐occurrence Matrix; Image classification; Image databases; Image enhancement; Low level; Lung cancer; Medical imaging
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13607

Lung cancer is relying as one of the significant and leading cause for the deaths which are based on cancer. So, an effective diagnosis is a crucial step to save the patients who are all dying due to lung cancer. Moreover, the diagnosis must be performed based on the severity of lung cancer and the severity can be addressed with the help of an optimal classification approach. So, this research introduced an Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing to perform in‐depth classification of the disease affected partitions of the image and results in better classification results. The attention layer with dispersed dynamic routing evaluates the digit capsule from feature vector in a constant manner. As the first stage, data acquisitioned from datasets such as Lung Nodule Analysis‐16 (LUNA‐16), The Cancer Imaging Archive (TCIA) dataset and Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI). After acquisitioning data, pre‐processing is done to enhance the resolution of the image using Generative Adversarial Network. The pre‐processed output is given as output for extraction of features that takes place using GLCM and VGG‐16 which extracts the low level features and high level features respectively. Finally, categorization of lung cancer is performed using Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing which categorize the lung cancer as benign and malignant. The results obtained through experimental analysis exhibits that proposed approach attained better accuracy of 99.57%, 99.91% and 99.29% for LUNA‐16, LIDC‐IDRI and TCIA dataset respectively. The classification accuracy achieved by the proposed approach for LUNA‐16 dataset is 99.57% which is comparably higher than DBN, 3D CNN, Squeeze Nodule Net and 3D‐DCNN with multi‐layered filter with accuracies of 99.16%, 97.17% and 94.1% respectively.