results: 研究结果显示,提案的方法可以准确检测肺癌区域在 CT 图像中。Abstract
Lung cancer is one of the prevalence diseases in the world which cause many deaths. Detecting early stages of lung cancer is so necessary. So, modeling and simulating some intelligent medical systems is an essential which can help specialist to accurately determine and diagnose the disease. So this paper contributes a new lung cancer detection model in CT images which use machine learning methods. There are three steps in this model: noise reduction (pre-processing), segmentation (middle-processing) and optimize segmentation for detect exact are of nodules. This article use some filters for noise reduction and then use Independent Recurrent Neural Networks (IndRNN) as deep learning methods for segmentation which optimize and tune by Genetic Algorithm. The results represented that the proposed method can detect exact area of nodules in CT images.
摘要
lung cancer 是世界上一种非常普遍的疾病,导致许多人死亡。早期诊断lung cancer非常重要。因此,建模和模拟一些智能医疗系统是必要的,以帮助专家准确地诊断疾病。本文提出了一种新的肺癌检测模型,用于CT图像中检测肺癌。该模型包括三个步骤:噪声减少(预处理)、分割(中处理)和优化分割以寻找精确的肿体部分。本文使用了一些 filters 来减少噪声,然后使用独立Recurrent Neural Networks(IndRNN)作为深度学习方法进行分割,并通过遗传算法进行优化和调整。结果表明,提议的方法可以准确地检测CT图像中的肿体部分。
results: 该研究实现了无漫游 Aberration-free 全色图像捕集,并达到了与真实图像相同的高分辨率。Abstract
Recent advances in metasurface lenses (metalenses) have shown great potential for opening a new era in compact imaging, photography, light detection and ranging (LiDAR), and virtual reality/augmented reality (VR/AR) applications. However, the fundamental trade-off between broadband focusing efficiency and operating bandwidth limits the performance of broadband metalenses, resulting in chromatic aberration, angular aberration, and a relatively low efficiency. In this study, a deep-learning-based image restoration framework is proposed to overcome these limitations and realize end-to-end metalens imaging, thereby achieving aberration-free full-color imaging for massproduced metalenses with 10-mm diameter. Neural network-assisted metalens imaging achieved a high resolution comparable to that of the ground truth image.
摘要