Abstract
Lung cancer has one of the highest cancer mortality rates in the world and threatens people’s health. Timely and accurate diagnosis can greatly reduce the number of deaths. Therefore, an accurate diagnosis system is extremely important. The existing methods have achieved significant performances on lung cancer diagnosis, but they are insufficient in fine-grained representations. In this paper, we propose a novel attentive method to differentiate malignant and benign pulmonary nodules. Firstly, the residual attention network (RAN) and squeeze-and-excitation network (SEN) were utilized to extract spatial and contextual features. Secondly, a novel multi-scale attention network (MSAN) was proposed to capture multi-scale attention features automatically, and the MSAN integrated the advantages of the spatial attention mechanism and contextual attention mechanism, which are very important for capturing the salient features of nodules. Finally, the gradient boosting machine (GBM) algorithm was used to differentiate malignant and benign nodules. We conducted a series of experiments on the Lung Image Database Consortium image collection (LIDC-IDRI) database, achieving an accuracy of 91.9%, a sensitivity of 91.3%, a false positive rate of 8.0%, and an F1-score of 91.0%. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods with respect to accuracy, false positive rate, and F1-Score.
Highlights
Lung cancer is one of the most lethal cancers in the world, posing a threat to people’s health [1]
The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods with respect to accuracy, false positive rate, and F1-Score
In the 1960s, computer-aided diagnosis (CAD) was proposed and used to diagnose lung cancer, which relieved the pressure on doctors and helped them to diagnose cases more accurately [3]
Summary
Lung cancer is one of the most lethal cancers in the world, posing a threat to people’s health [1]. Lung cancer mortality can be significantly reduced through early diagnosis and screening [2]. In the 1960s, computer-aided diagnosis (CAD) was proposed and used to diagnose lung cancer, which relieved the pressure on doctors and helped them to diagnose cases more accurately [3]. Researchers manually extracted hand-crafted features and designed classifiers, but designing hand-crafted features was time-consuming and required professional medical knowledge. The effectiveness of feature extraction depends on doctors’ expertise in lung cancer diagnosis and their understanding of traditional machine learning methods. Hand-crafted features were subjective and their generalization was poor
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