Hybrid feature selection model for classification of lung disorders

• Published in: Journal of ambient intelligence and humanized computing Vol. 13; no. 12; pp. 5609 - 5625
• Main Authors: Dharmalingam, Vivekanandan, Kumar, Dhananjay
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Classification; Computational Intelligence; Computed tomography; Datasets; Engineering; Feature selection; Image classification; Lung diseases; Lungs; Methods; Multiple objective analysis; Normal distribution; Numerical analysis; Optimization; Original Research; Particle swarm optimization; Robotics and Automation; Search algorithms; Sensitivity; Statistical analysis; Support vector machines; Swarm intelligence; Tabu search; User Interfaces and Human Computer Interaction; Multi-objective particle swarm optimization; Feature selection; Tabu search; Multi-objective particle swarm optimization with a local Tabu search (MOPSO-TS); k-Nearest neighbor
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-021-03224-7

Feature selection in computer aided diagnosis is now becoming a challenging part in the classification of lung diseases. This is because, it needs to deliver results with improved accuracy and it also requires a greater number of features for analysis. The major demerit of widely utilized single-objective feature selection (FS) algorithm is that it proffers only a single optimum solution for a feature set. Here, a hybridized multi-objective particle swarm optimization with a local Tabu search (MOPSO-TS) algorithm is proposed to overcome the above demerit of the traditional single objective algorithm by producing a bag of optimum solutions which trade disparate objectives amongst themselves. The work is validated against a feature set which consists of GLCM features, shape features and GLRLM (gray-level run length matrix) extracted from lung chest tomography (CT) images. Classification is done using k-nearest neighbor with class probability and normal distribution (ND). The proposed FS method’s performance is analyzed against widely used bio-inspired FS methods such as Firefly, Particle Swarm Optimization along with Bee Colony Optimization algorithms. The numerical analysis of this model indicates that the proposed hybrid FS algorithm achieves improved performance compared to a single objective optimization algorithm in respect of specificity, accuracy, F-score, precision, sensitivity and error rate. The proposed algorithm obtains the result of 90.588% in both and specificity accuracy rate, (77.143) precision, 87.667 sensitivity rate and error rate of 0.1 which are higher on considering the other prevailing methodologies