Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps From Colorectal Ct Images Publisher



Harchegani HB¹ ; Moghaddasi H²
Source: International Journal of Preventive Medicine Published:2024

Abstract

Background: Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer‑aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer. Method: First, the data set of the research was created from the colorectal CT images of the patients of Loghman‑e Hakim Hospitals in Tehran and Al‑Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model. Results: The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al‑Zahra Hospitals was 0.044 and 0.021, respectively. Conclusions: The results of statistical tests and research variables show the efficiency of the CTC‑CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images. © 2024 International Journal of Preventive Medicine.