Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer‐aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel‐based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically.

中文翻译：

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具有纳米级成像的超像素和增强的深度卷积神经网络概念用于肺肿瘤分类

肺肿瘤是一种由不规则肺细胞生长引起的复杂疾病。早期肿瘤检测是有效治疗计划的关键因素。在评估肺部计算机断层扫描时，医生在确定精确的肿瘤边界时有很多困难。通过向放射科医师提供第二意见并帮助提高肿瘤检测的灵敏度和准确性，计算机辅助诊断的使用可能会同样有效。在这篇研究文章中，提出的肺肿瘤检测算法包括四个阶段：图像采集、预处理、分割和分类。基于Advance Target Map Superpixel的区域分割算法被提出用于分割目的，然后使用纳米成像理论测量肿瘤区域。使用boosted深度卷积神经网络的概念产生97.3%的精度，可以实现图像识别。在使用当前方法的文献类型中，显示了该研究提出的功效，所提出的方法的实施被显着地发现。