Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

Abstract

Lung cancer is the most deadly type of cancer and it is caused by genetic variations in lung tissues. Other causes of lung cancer are alcohol, smoking, and threatening gas exposures. The diagnosis of lung cancer is an intricate task and early detection of lung cancer can help to get the exact treatment in advance. The application of a computer-aided diagnosis process helps to predict lung cancer earlier, nonetheless, it does not provide better accuracy. The overfitting nature of features and dimensionality of lung cancer can prevent it from obtaining maximum accuracy. Hence, we proposed a novel faster region convolutional neural network (RCNN) based fuzzy butterfly optimization algorithm (FBOA) to achieve better prediction accuracy and effectiveness. The proposed Faster RCNN can provide better positioning of lung cancer swiftly and effectively and the FBOA approach can be used to perform two-stage classification. The fuzzy rules used in the FBOA can be utilized to find the severity of the lung cancer and differentiate the Benign stage and Malignant stage effectively.The experimental analyses are performed in MATLAB simulation. The preprocessing of images is performed by different tools from MATLAB and is to format the images as required. The cancer imaging archive (TCIA) dataset is utilized to analyze the performance of the proposed method and compared with various state-of-art works. The performance of the proposed method is evaluated by using different evaluation metrics such as precision, recall, F-measure, and accuracy and attained 99, 98, 99, and 97% respectively. Thus, our proposed method outperforms all the other approaches.

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

“…Due to the increased complexity and low convergence, the proposed work intends to utilize a novel and intelligent optimization algorithm, named as, LBO for feature selection. This technique is developed based on the standard Lion optimization [26] and butterfly optimization [27] algorithms [33]. Typically, butterfly optimization algorithm's foraging and mating behaviors are similar to those of butterflies.…”