The classification of lung cancer: a DenseNet feature-based transfer learning evaluation - Arzmi, Mohd Hafiz; P.P. Abdul Majeed, Anwar; Musa, Rabiu Muazu; Mohd Razman, Mohd Azraai; Gan, Hong-Seng; Mohd Khairuddin, Ismail; Ab. Nasir, Ahmad Fakhri (Springer, 2023)
In the present study, a class of deep learning approaches is used to clas- sify non-small-cell lung cancers. A total of 400 computed tomography (CT) images of lung cancer that are demarcated into normal, large cell carcinoma, adenocarci- noma and squamous cell carcinoma are split into the 70:15:15 ratio for training, testing and validation. The images are evaluated on different DenseNet-Support Vector Machine (SVM) pipelines, i.e., DenseNet121-SVM, DenseNet169-SVM and DenseNet201-SVM, respectively. It was shown from the present investigation that the DenseNet121-SVM pipeline is able to yield a test classification accuracy of 87%. Therefore, it could be demonstrated that the proposed architecture is able to classify the different variations of non-small-cell lung cancers reasonably well and could further facilitate the diagnosis of lung cancer by clinicians.