Improvement of Lung Nodule Detection Algorithm Based on Deep Learning YOLOv5
Machine learning and deep learning are currently the most common and effective methods for solving target recognition problems.Machine learning achieves prediction and judgement of unknown data by learning from existing data and constructing models.Deep learning is a special form of machine learning,which achieves modelling and solving complex problems by constructing multi-layer neural network models.In target recognition tasks,machine learning and deep learning can achieve automatic recognition of targets by learning image features and training classifiers or detectors.Lung nodules are a common sign of early lung cancer,and accurate and effective detection and identification of lung nodules is crucial for the diagnosis and treatment of early lung cancer.Traditional lung nodule detection methods often rely on hand-designed feature extractors and classifiers,whose performance is limited by the ability of feature representation and generalisation.The rise of deep learning techniques,on the other hand,has brought new opportunities for lung nodule detection.Medical image processing and deep learning techniques have made significant progress in the detection and recognition of lung nodules.In this study,we propose an improved lung nodule detection method based on deep learning YOLOv5 algorithm,which can efficiently and accurately detect lung nodules with low false and missed detection rates.
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