Department of ECE, Annamacharya Institute of Technology and Sciences, Rajampet, India.
Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India.
Front Public Health. 2022 Jun 7;10:769692. doi: 10.3389/fpubh.2022.769692. eCollection 2022.
One of the most common causes of death from cancer for both women and men is lung cancer. Lung nodules are critical for the screening of cancer and early recognition permits treatment and enhances the rate of rehabilitation in patients. Although a lot of work is being done in this area, an increase in accuracy is still required to swell patient persistence rate. However, traditional systems do not segment cancer cells of different forms accurately and no system attained greater reliability. An effective screening procedure is proposed in this work to not only identify lung cancer lesions rapidly but to increase accuracy. In this procedure, Otsu thresholding segmentation is utilized to accomplish perfect isolation of the selected area, and the cuckoo search algorithm is utilized to define the best characteristics for partitioning cancer nodules. By using a local binary pattern, the relevant features of the lesion are retrieved. The CNN classifier is designed to spot whether a lung lesion is malicious or non-malicious based on the retrieved features. The proposed framework achieves an accuracy of 96.97% percent. The recommended study reveals that accuracy is improved, and the results are compiled using Particle swarm optimization and genetic algorithms.
对于男性和女性来说，癌症导致死亡的最常见原因之一是肺癌。肺结节是癌症筛查的关键，早期发现可以进行治疗，并提高患者的康复率。尽管在这一领域已经做了很多工作，但仍需要提高准确性，以提高患者的坚持率。然而，传统系统不能准确地对不同形式的癌细胞进行分割，也没有系统能够达到更高的可靠性。本工作提出了一种有效的筛选程序，不仅可以快速识别肺癌病变，而且可以提高准确性。在这个过程中，利用 Otsu 阈值分割完成对选定区域的完美隔离，利用布谷鸟搜索算法为分割癌症结节定义最佳特征。通过使用局部二值模式，可以检索病变的相关特征。基于检索到的特征，CNN 分类器用于判断肺病变是恶性的还是非恶性的。所提出的框架实现了 96.97%的准确率。推荐的研究表明，准确性得到了提高，结果是使用粒子群优化和遗传算法编译的。
