Classification of Lung Nodules based on Transfer Learning with K-Nearest Neighbor (KNN)

Trishna Saikia, Malho Hansdah, Koushlendra Kumar Singh, Manish Kumar Bajpai

Abstract: Lung is a major part of our body. Any disease in the lung can affect the whole respiratory system. Lung cancer is considered one of the most dangerous types of cancer where the death rate is relatively more than other types of cancer. It can be diagnosed in an appropriate manner only if lung nodules are classified at an early stage of its growth. A Computer-Aided Diagnosis (CAD) system can improve the accuracy of properly classifying the cancerous cells which will surely increase the survival rate of patients. This research study has proposed a transfer learning approach for the classification of lung tumors into four classes, which are Small-cell-carcinoma, Adenocarcinoma, Squamous-cell-carcinoma, and Large cell-carcinoma. A large-scale CT and PET/CT DICOM images containing datasets have been used for this research work. This research article has proposed a novel approach that includes a combination of four different advance deep Convolutional Neural Networks: VGG-16, VGG-19, Inception-V3, and Mobilenet-V2 with the K- Nearest Neighbor (KNN) algorithm for the classification of lung cancer into four classes. This research experiment consists of four stages namely image preprocessing, feature generation, feature extraction, and classification. VGG-16 with the KNN algorithm has given the highest validation accuracy as compared to other models for the classification of lung nodules. The proposed model has been evaluated by using a total of 4105 medical images belonging to four categories.