Reddy Ummadi Janardhan, Ramana Reddy Busi Venkata, Reddy Boddi Eswara
Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India.
Department of Computer Science & Engineering, Narayana Engineering College (NEC), Nellore, India.
Curr Med Imaging Rev. 2019;15(4):405-413. doi: 10.2174/1573405614666180212162727.
Lung Cancer is the disease spreading around the world nowadays. Early recognition of lung disease is a difficult task as the cells which cause tumor will grow quickly and the majority of these cells are enclosed with each other. From the beginning of the treatment, tumor detection handling systems which are generally utilized for the diagnosis of lung cancer, recognizable proof of hereditary and ecological elements is imperative in creating a novel technique for lung tumor detection. In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images.
In this proposed framework, GLCM (Gray Level Co-event Matrix) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM (Gray Level Co-event Matrix) features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increment survival rate.
RESULTS & CONCLUSION: The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In this manuscript, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features. In this manner, early discovery and probability of lung cancer should assume a crucial task in finding a procedure and furthermore, an increment in the survival rate of the patient. This exploration investigates machine learning systems which consider quality articulation, to perceive cancer or to identify lung cancer.
肺癌是当今在全球范围内蔓延的疾病。由于导致肿瘤的细胞生长迅速且大多数细胞相互包裹，早期识别肺部疾病是一项艰巨的任务。从治疗开始，对于通常用于诊断肺癌的肿瘤检测处理系统而言，识别遗传和环境因素对于开发一种新型的肺癌检测技术至关重要。在不同的癌症中，例如肺癌，计算时间对于在目标图像中发现异常问题至关重要。
在这个提出的框架中，灰度共生矩阵（GLCM）用于图像预处理和特征提取过程，以检查患者的状况是正常还是异常。基于表面的特征，例如灰度共生矩阵（GLCM）特征，在用于肺癌识别的补救性图像检查中起着至关重要的作用。如果肺癌在其早期阶段能够被有效识别和预测，那么它会减少许多治疗选择，此外，还会降低侵入性手术的风险并提高生存率。
所提出的方法将使用概率框架有效地识别肺部肿瘤的位置。这将为肺癌的识别和诊断提供一个有前景的结果。在本手稿中，灰度共生矩阵（GLCM）特征用于预测肺部肿瘤，并与直方图和GLCM特征进行性能分析测试，其中GLCM特征在预测肺部肿瘤方面更准确，但比直方图特征花费的时间更多。通过这种方式，肺癌的早期发现和概率在寻找一种方法中应发挥关键作用，并且还能提高患者的生存率。本研究调查了考虑特征表达的机器学习系统，以识别癌症或肺癌。
