Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

• Published in: Multimedia tools and applications Vol. 83; no. 31; pp. 76807 - 76831
• Main Authors: Sudha, R., Maheswari, K. M. Uma
• Format: Journal Article
• Language: English
• Published: New York Springer US 2024; Springer Nature B.V
• Subjects: Computed tomography; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diagnosis; Image contrast; Image enhancement; Image filters; Lung cancer; Machine learning; Medical imaging; Multimedia Information Systems; Nodules; Optimization; Particle swarm optimization; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Malignant nodules; Optimized mask RCNN; Particle swarm optimization; Lung cancer; Benign nodules; Snake swarm optimization
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-19113-y

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans; however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS 2 OA). The proposed PS 2 OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately.