A novel hybrid deep learning method for early detection of lung cancer using neural networks

Abstract

Lung cancer is a fatal disease with a high mortality rate in diseased patients. Early diagnosis of this disease and accurately identifying the lung cancer stage can save the patients’ lives. Several image processing, biomarker-based and machine automation approaches are used to identify lung cancer, but accuracy and early diagnosis are challenging for medical practitioners. The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) are utilized in this study to extract the CT scan images. In conventional methods, manual CT images are supplied to visualize whether the person has lung cancer. This research article proposes a novel method for an early and accurate diagnosis called Cancer Cell Detection using Hybrid Neural Network (CCDC-HNN). The features are extracted from the CT scan images using deep neural networks. The accuracy in feature extraction is very important to detect the cancerous cells at early stages to save the patient from this fatal disease. In this study, an advanced 3D-convolution neural network (3D-CNN) is also utilized to improve the accuracy of diagnosis. The suggested approach also enables the distinction between benign and malignant tumors. The results are evaluated using standard statistical techniques, and the results confirm the viability of the proposed hybrid deep learning (DL) technique for early diagnosis of the lung cancer.