The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat
to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to …

Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary
nodules are the primary manifestation of early-stage lung cancer. If patients can be detected …

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …

Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …

Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung
cancer, enhancing patient survival possibilities. A number of nodule segmentation …