A Review on Diagnosis of Lung Cancer and Lung Nodules in Histopathological Images using Deep Convolutional Neural Network

P. Shimna, A. Shirly Edward, T. Roshini
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Abstract

Lung cancer is a serious health issue that requires early detection. Machine Learning has figured prominently in the health sector in general, and in analyzing histopathological images and detecting illnesses in particular, because it may eliminate many mistakes that may arise when radiologists analyse image data. Traditional healthcare imaging techniques such as x-rays, CT scans, MRIs, and so on have little promise for detecting lung tumours. Convolutional Neural Networks have piqued the interest of doctors and academics due to their ability to analyse images accurately. The current study examines the role of CNN in lung cancer detection. Findings presented in the literature provide prospective researchers with a deeper understanding of the issue. We examined most of the features and includes extensive recommendations for future study. The primary purpose of this study is to detect malignant lung nodules in a lung image and to categorize pulmonary cancer. This work concentrates on novel Deep Learning techniques used in literature to locate cancerous lung nodules.
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深度卷积神经网络在肺癌及肺结节病理图像诊断中的研究进展
肺癌是一种严重的健康问题,需要及早发现。机器学习在整个卫生部门,特别是在分析组织病理学图像和检测疾病方面占有重要地位,因为它可以消除放射科医生分析图像数据时可能出现的许多错误。传统的医疗成像技术,如x射线、CT扫描、核磁共振等,在检测肺部肿瘤方面几乎没有希望。卷积神经网络因其准确分析图像的能力而引起了医生和学者的兴趣。目前的研究探讨了CNN在肺癌检测中的作用。在文献中提出的研究结果为未来的研究人员提供了对这个问题更深入的理解。我们研究了大多数特征,并为未来的研究提供了广泛的建议。本研究的主要目的是检测肺部图像中的恶性肺结节,并对肺癌进行分类。这项工作集中在文献中用于定位癌性肺结节的新颖深度学习技术上。
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