Lung cancer histopathology image classification using transfer learning with convolution neural network model

Abstract

BACKGROUND: Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. OBJECTIVE: This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. METHODS: The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model. RESULTS: The feature extraction was performed by applying transfer learning using EfficientNetB7 as pretrained model. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy. CONCLUSION: The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients.