eess.IV

eess.IV - 2023-12-05

Lung Cancer Detection from CT Scan Images based on Genetic-Independent Recurrent Deep Learning

  • paper_url: http://arxiv.org/abs/2312.03185
  • repo_url: None
  • paper_authors: Ehsan Sadeghi Pour, Mahdi Esmaeili
  • for: 这篇论文是为了探讨一个新的肺癌检测模型，用于检测肺癌在 computed tomography（CT）图像中的早期阶段。
  • methods: 这个模型包括三个步骤：噪音范围删除（预处理）、分类（中间处理）和优化分类以找到精确的肺癌区域。 这里使用了一些滤波器来删除噪音，然后使用独立随机神经网络（IndRNN）作为深度学习方法进行分类，并通过生物学遗传探索来优化和调整。
  • 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。

Deep-learning-driven end-to-end metalens imaging

  • paper_url: http://arxiv.org/abs/2312.02669
  • repo_url: https://github.com/yhy258/eidl_drmi
  • paper_authors: Joonhyuk Seo, Jaegang Jo, Joohoon Kim, Joonho Kang, Chanik Kang, Seongwon Moon, Eunji Lee, Jehyeong Hong, Junsuk Rho, Haejun Chung
  • for: 这个论文旨在超越现有的媒体镜（metalens）技术，实现端到端的媒体镜图像处理，并实现覆盖镜像干扰的全色图像捕集。
  • methods: 该研究提出了一种基于深度学习的图像修复框架，用于缓解媒体镜的基本负面效应，包括色差畸形、角度畸形和效率较低。
  • 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