Computer aided model for lung cancer classification using cat optimized convolutional neural networks

Abstract

Lung cancer is a dangerous illness in the modern world. Lung cancer diagnosis requires a more thorough investigation than other disease processes since it affects both men and women and has a higher fatality rate. The law currently mandates the creation of sophisticated expert systems for clinical analysis and the precise identification of lung disease therapy. Images from a Computer Tomography (CT) scan can give more useful information about a lung cancer diagnosis. CT scan input pictures are used in the formulation of several Machine Learning (ML) and Deep Learning (DL) algorithms for the enhancement of diagnosis and treatment procedures. The most challenging aspect of research, however, is still creating an accurate and sophisticated system. This study proposes Deep Fused Features-Based Cat-Optimized Networks (DFF-CON), a novel classification approach that utilizes the concepts of fused features and optimized networks. In the proposed structure, Deep Convolutional Neural Networks (DCNN) are employed to enhance the classification maps and ultimately decrease the probability of an overfitting issues. In the framework, saliency maps are used as a first-tier segmentation technique. In order to get the best diagnosis for malignancies in lung CT scan pictures, the suggested study additionally substituted a cat-optimized optimized CNN for the standard neural network. The suggested solution has been developed using Python 3.8, the Keras API, and Tensorflow 1.8 together. With the LIDC-IDRI image datasets, a wide range of performance measures are created and examined for thorough testing, including accuracy, sensitivity, specificity, precision, and the f1-score. Results from the simulation indicate that the suggested framework achieves high accuracy (Ac) (99.89 %), sensitivity (Sy) (99.8 %), specificity (Sy) (99.76 %), precision (Pn) (99.8 %) and F1-score (Fr) (99.88 %). Then, an assessment with other current models is conducted to demonstrate the benefits of the presented framework.