Automatic Lung Cancer Detection Using Computed Tomography Based on Chan Vese Segmentation and SENET

Abstract

Lung cancer is the most common cancer and the primary reason for cancer related fatalities globally. Lung cancer patients have a 14% overall survival rate. If the cancer is found in the early stages, the lives of patients with the disease may be preserved. A variety of conventional machine and deep learning algorithms have been developed for the effective automatic diagnosis of lung cancer. But they still have issues with recognition accuracy and take longer to analyze. To overcome these issues, this paper presents deep learning assisted Squeeze and Excitation Convolutional Neural Networks (SENET) to predict lung cancer on computed tomography images. This paper uses lung CT images for prediction. These raw images are preprocessed using Adaptive Bilateral Filter (ABF) and Reformed Histogram Equalization (RHE) to remove noise and enhance an image’s clarity. To determine the tunable parameters of the RHE approach Tuna Swam optimization algorithm is used in this proposed method. This preprocessed image is then given to the segmentation process to divide the image. This proposed approach uses the Chan vese segmentation model to segment the image. Segmentation output is then fed into the classifier for final classification. SENET classifier is utilized in this proposed approach to final lung cancer prediction. The outcomes of the test assessment demonstrated that the proposed model could identify lung cancer with 99.2% accuracy, 99.1% precision, and 0.8% error. The proposed SENET system predicts CT scanning images of lung cancer successfully.