An ensemble method for the detection and classification of lung cancer using Computed Tomography images utilizing a capsule network with Visual Geometry Group

• Published in: Biomedical signal processing and control 2023-08, Vol.85, p.104930, Article 104930
• Main Authors: Bushara, A.R., Vinod Kumar, R.S., Kumar, S.S.
• Format: Article
• Language: English
• Subjects: Capsule network; Computed Tomography; Convolutional Neural Network; Deep learning; Lung cancer
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104930

•In Section 1-Introduction Motivation of the Research Work is included.•Research Gap is included in Section 2-Related Works.•The proposed method is simulated with one more dataset, Kaggle.•The proposed architecture is simulated using different configurations.•The manuscript has been edited and reworked to incorporate reviewers’ suggestions. The five-year survival rate for lung cancer is among the lowest of all malignancies. Lung cancer possesses a high incidence of death per capita, therefore finding it early is crucial. To this end, Computed Tomography (CT) scans are often employed for the early identification of lung cancer, with clinical judgement serving as the current reference standard. Deep learning Convolutional Neural Networks (CNNs)have been used in end-to-end approaches for the detection of lung nodules. Capsule Networks are one of the numerous deep learning models that have been presented as a potential solution to the problems caused by the shortcomings of CNNs, such as the inability of CNNs to recognize fine-grained spatial correlations. As of now, capsule networks have shown to be effective in solving medical imaging challenges. To build on the previous work, Visual Geometry Group - Capsule Network (VGG-CapsNet) an innovative capsule network-based combination of VGG and Capsule Network is introduced. According to the findings, VGG-CapsNetis superior to using a basiccapsule network, or a combination ofCNNcapsule networks, with a 0.980 AUC and a 98.61 % F1-Score, a precision of 99.07 %, a recall of 98.16 %, a specificity of 99.07 %,and an accuracy of 98.61 % for LIDC-IDRI datasets, and 98.14 % precision, 99.16 specificity, 98.07 % accuracy, 0.98 AUC and 98.14 % F1-Score for Kaggle datasets.