PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

• Published in: BioMed research international Vol. 2022; no. 1; p. 3618197
• Main Authors: Praveen, Sheeba, Tyagi, Neha, Singh, Bhagwant, Karetla, Girija Rani, Thalor, Meenakshi Anurag, Joshi, Kapil, Tsegaye, Melkamu
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Cancer; Classification; Computer-aided medical diagnosis; Datasets; Decision trees; Diagnosis; Efficiency; Feature selection; Genetic algorithms; Head & neck cancer; Head and neck cancer; Heuristic; Histology; Humans; Lung cancer; Mathematical optimization; Medical imaging; Medical prognosis; Mesothelioma; Mesothelioma, Malignant; Methods; Optimization; Particle swarm optimization; Patients; Tumors; India
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2022/3618197

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.