In the contemporary era,the death rate is increasing due to lung cancer.However,technology is continuously enhancing the quality of well-being.To improve the survival rate,radiologists rely on Computed Tomography(CT)s...In the contemporary era,the death rate is increasing due to lung cancer.However,technology is continuously enhancing the quality of well-being.To improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung nodules.This paper presented a detailed,systematic review of several identification and categorization techniques for lung nodules.The analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)algorithm.The findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer detection.This paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer diagnosis.Moreover,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated CNNs.Interestingly,while accepting the strides formed by CAD systems,the review addressed persistent challenges.展开更多
文摘In the contemporary era,the death rate is increasing due to lung cancer.However,technology is continuously enhancing the quality of well-being.To improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung nodules.This paper presented a detailed,systematic review of several identification and categorization techniques for lung nodules.The analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)algorithm.The findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer detection.This paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer diagnosis.Moreover,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated CNNs.Interestingly,while accepting the strides formed by CAD systems,the review addressed persistent challenges.
