Improving YOLOv6 using advanced PSO optimizer for weight selection in lung cancer detection and classification

The human body comprises various essential organs, including the lungs, which are responsible for the exchange of gases during respiration. However, the modern lifestyle and environmental pollution have contributed to the deterioration of lung health. To address this issue, The medical field has extensively investigated image processing methods for the identification and examination of lung diseases. In our research, we have employed a specific Convolutional Neural Network (CNN) architecture known as You Look Only Once (YOLO) Version 6 for lung cancer detection. We have further optimized the CNN weights using Particle Swarm Optimization(PSO) with dual adaptation and dual validation. The proposed method involves preprocessing the LUNA 16 Challenge Dataset. The dataset used in this study, known as LUNA-16, comprises CT scans obtained from a cohort of 888 patients. In total, the dataset includes 1,181 individual scans, providing a substantial amount of data for analysis and evaluation. Our objective is to develop a model capable of classifying individuals as either having lung cancer or not. Remarkably, our model achieves an accuracy of 82.79% on the LUNA 16 Dataset, which is significant improvement from the previous used methodologies like ANN with 74% accuracy and YOLOv3 with an accuracy of 80.6% showcasing its effectiveness in predicting lung cancer.