改变人类的生活方式，导致或加剧了许多疾病。其中一种疾病是癌症，所有类型的癌症中，如脑癌和肺癌，肺癌是致命的。计算机辅助诊断（CAD）系统可以早期检测到这些肿瘤，以挽救生命。CT扫描医学图像是最好的图像之一，可在肺部检测到这些肿瘤，特别受医生们的认可。然而，肿瘤的位置和随机形状，以及CT扫描图像的质量差，是医生在识别这些肿瘤时面临的最大挑战之一。因此，深度学习算法备受研究人员的推崇。本文提出了一种基于卷积神经网络算法的CT扫描图像中识别肿瘤和肺部结节的新方法，该方法能够准确地识别肿瘤。活动计数算法将显示检测到的肿瘤。所提出的方法通过敏感性评估标准和Dice相似性标准进行定性评估。所得到的结果显示优越性，准确性为98.33%，有效性为99.25%，Dice相似性标准为98.18%。

Changing the human being's lifestyle, has caused, or exacerbated many diseases. One of these diseases is cancer, and among all kind of cancers like, brain and pulmonary; lungs cancer is fatal. The cancers could be detected early to save lives using Computer Aided Diagnosis (CAD) systems. CT scans medical images are one the best images in detecting these tumors in lung that are especially accepted among doctors. However, location and random shape of tumors, and the poor quality of CT scans images are one the biggest challenges for physicians in identifying these tumors. Therefore, deep learning algorithms have been highly regarded by researchers. This paper presents a new method for identifying tumors and pulmonary nodules in CT scans images based on convolution neural network algorithm with which tumor is accurately identified. The active counter algorithm will show the detected tumor. The proposed method is qualitatively measured by the sensitivity assessment criteria and dice similarity criteria. The obtained results with 98.33% accuracy 99.25% validity and 98.18% dice similarity criterion show the superiority of the proposed method.