International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
YOLOv8-Based Lung Nodule Detection: A Novel Hybrid Deep Learning Model Proposal CİHAT ŞAMAN, ŞERİFE ÇELİKBAŞ 1 BIOMEDİCAL ENGINEER , SMNWAY INC & cihatsaman@smnway.com, TURKEY
2LECTURER, İSTANBUL AYDIN UNIVERSITY & serifecelikbas@aydin.edu.tr, TURKEY
---------------------------------------------------------------------***--------------------------------------------------------------------can be estimated by using validated models that Abstract - Small oval or circular masses identified in the
incorporate radiographic and clinical features. The size, shape, density, location, and growth rate of the nodules are important radiographic characteristics that influence risk assessment and follow-up recommendations. The use of low-dose computed tomography (LDCT) for lung cancer screening has increased the detection of pulmonary nodules, especially those that are sub-solid or groundglass in appearance, which have a higher risk of cancer than solid nodules. New criteria for the classification and management of atypical pulmonary cysts, juxta pleural nodules, and inflammatory or infectious findings have been introduced in Lung-RADS® v2022, a standardized reporting system for LDCT screening. The goal of pulmonary nodule evaluation is to identify and treat lung cancer at an early stage while minimizing unnecessary interventions and harm for benign nodules [1-3].
lungs are known as lung nodules. When these nodules are smaller than 3 cm, they are often considered less concerning; however, over time, they may increase in size, potentially leading to more serious consequences. Early detection of these nodules and timely preventive measures are crucial to impede their progression to malignancy. Conventional diagnostic methods like computerized tomography (CT) and radiographic imaging techniques are utilized for this purpose. Nevertheless, these approaches can either subject patients to excessive radiation or prove inadequate in detecting small nodules. As a result, various deep learning-based image processing techniques are being explored for lung nodule detection. In this study, a novel deep-learning model is proposed for the automated realtime detection of lung nodules. The proposed model exhibits a remarkable accuracy of 92.3% in nodule detection, along with a sensitivity of 88.5% and a mean average precision (mAP) of 53.5%. The model is built using the YOLOv8 architecture, with the YOLOv8m configuration yielding the best results. Additionally, graphical comparisons with existing studies in the literature demonstrate the effectiveness of the training model.
Lung cancer is one of the most common and deadliest types of cancer in the world [4]. Early diagnosis and treatment of lung cancer is critical to improve survival. For this purpose, developed computational image processing studies are carried out. However, lung nodule detection and diagnosis poses challenges for image processing and analysis. Lung nodules can vary greatly in size, shape, density, location, and number. In addition, factors such as noise, artifact, and lack of contrast in images may complicate the detection and characterization of lung nodules. Lung nodule detection using the deep learning method is an important step for early detection of lung cancer. Deep learning techniques for lung nodule detection provide higher performance and accuracy than traditional computer-aided diagnostic systems. Zhang et al. [5] utilized 3D DenseNet and 3D FPN models for detecting lung nodules. They designed a dense feature pyramid network to extract multi-scale features from different layers of the DenseNet backbone. In their study, they achieved an impressive competition performance metric (CPM) value of 0.8934 on the LUNA16 dataset, showcasing compelling results. Furthermore, the detection performance was improved by approximately 2% compared to other methods Zhang et al. [6] proposed an integrated active contour model (IACM_MRFEBPD) for small ground glass opacity (GGO) pulmonary nodule segmentation. The method combines Markov random field energy and Bayesian probability difference, achieving an average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI
Key Words: deep learning, lung cancer detection, yolov8x, yolov8l, yolov8m, yolov8s, yolov8n
1. INTRODUCTION Pulmonary nodules are small, round, or oval-shaped growths in the lungs that can be benign or malignant. They are often detected incidentally on chest imaging or through dedicated screening programs for lung cancer. The presence of pulmonary nodules may cause symptoms such as cough, chest pain, shortness of breath, or coughing up blood, especially if the nodules are large, multiple, or cancerous. Some nodules may also be associated with infections, inflammation, or autoimmune diseases that can affect other organs and systems. Therefore, determining the nature and cause of pulmonary nodules is essential for providing appropriate treatment and preventing complications. The epidemiology of pulmonary nodules depends on various factors, such as the prevalence of smoking, environmental exposures, infectious diseases, and genetic susceptibility. According to the latest guidelines and evidence, the management of pulmonary nodules should be based on the risk of malignancy, which
© 2023, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 230