Abstract:

Abstract: Lung cancer remains one of the most rapidly growing malignant diseases, posing a severe threat to public health due to its high morbidity and mortality rates. Early detection is critical, and CT imaging has proven effective in identifying lung cancer in its initial stages, often manifesting as pulmonary nodules. Low-Dose Computed Tomography (LDCT) has further enhanced the precision of detecting and classifying lung nodules, significantly reducing mortality rates. While radiologists play a key role in identifying lung nodules through image analysis, the growing demand and limited availability of specialists make manual assessments challenging. The increasing volume of CT data underscores the importance of employing efficient Computer-Assisted Detection (CAD) systems to automate the analysis of lung nodules. Convolutional Neural Networks (CNNs) have shown remarkable promise in facilitating early detection and management of lung cancer. This study reviews current methods for automated lung nodule detection, detailing experimental benchmarks and utilizing publicly available lung CT image datasets. Furthermore, it explores emerging research trends, challenges, and future directions in this field. The findings highlight the transformative impact of CNNs on early lung cancer diagnosis and treatment, offering valuable insights for medical research communities to enhance healthcare systems through advanced AI-driven methodologies.

Description:

.