Lung Disease Detection and Classification Using Single Shot Multi-Box Detector Network: A Comprehensive Study

Lung diseases significantly impact the world regarding health, economic cost, and social and psychological well-being. X-ray images are a primary method for diagnosing lung diseases, but the manual analysis of these images can be time-consuming, subjective, and prone to inaccuracies. However, it is essential to diagnose lung diseases in a timely manner and with high accuracy to ensure effective treatment and management. This study introduces an innovative deep-learning version termed the "ESSDN-LN model" to overcome these challenges. It is a variant of the single shot detector (SSD) network. This model aims to rapidly and accurately detect and classify six types of lung disease: aortic enlargement, cardiomegaly, pleural thickening, pulmonary fibrosis, COVID-۱۹, and pneumonia. The ESSDN-LD model was introduced in three versions: ESSDN-LDV۱, ESSDN-LDV۲, and ESSDN-LDV۳. ESSDN-LDV۱ incorporates the SSD with batch normalization, dropout regularization, and data augmentation techniques. ESSDN-LDV۲ builds upon the advancements of ESSDN-LDV۱ by incorporating the random search algorithm for adjusting model hyper-parameters and introducing the skip connections technique to enhance the detection performance. Furthermore, ESSDN-LDV۳ further enhances the capabilities of ESSDN-LDV۱ using the genetic algorithm for hyper-parameter tuning and incorporating feature fusion and skip connections techniques, thereby significantly improving the detection performance. The ESSDN-LDV۳ model demonstrated exceptional performance compared to other versions, achieving a remarkable accuracy of ۹۶.۵% and a prediction time of ۰.۰۱۸ seconds in the seven-class classification. Furthermore, it achieved a total accuracy of ۹۸.۴% and a prediction time of ۰.۰۱۳ seconds in the three-class classification, encompassing Covid-۱۹, pneumonia, and no-finding cases. These impressive results highlight the effectiveness and efficiency of the proposed method in accurately classifying lung diseases and can contribute to improved patient outcomes and treatment decisions.

کلیدواژه ها:

Detection Hyper ، parameters Lung diseases Optimizer Single shot detector