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首页> 外文期刊>International Journal of Engineering Science and Technology >A COMPUTER AIDED DIAGNOSIS SYSTEM FOR DETECTION OF LUNG CANCER NODULES USING EXTREME LEARNING MACHINE
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A COMPUTER AIDED DIAGNOSIS SYSTEM FOR DETECTION OF LUNG CANCER NODULES USING EXTREME LEARNING MACHINE

机译：计算机学习诊断系统的极端学习机肺癌结节

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摘要

The Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography (CT) images of chest. To produce a successful Computer Aided Diagnosis system, several problems has to be resolved. Segmentation is the first problem to be considered which helps in generation of candidate region for detecting cancer nodules. The second problem is identification of affected nodules from all the candidate nodules. Initially, the basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms are applied to the CT scan image in order to detect the lung region. Then the segmentation algorithm is applied in order to detect the cancer nodules from the extracted lung image. In this paper, Fuzzy Possibilistic C Mean (FPCM) algorithm is used for segmentation because of its accuracy. After segmentation, rule based technique is applied to classify the cancer nodules. Finally, a set of diagnosis rules are generated from the extracted features. From these rules, the occurrences of cancer nodules are identified clearly. The learning is performed with the help of Extreme Learning Machine (ELM) because of its better classification. For experimentation of the proposed technique, the CT images are collected from reputed hospital. The proposed system can be able to detect the false positive nodules accurately.
机译：本文提出了一种计算机辅助诊断（CAD）系统，用于通过对胸部计算机断层扫描（CT）图像进行分析来检测肺癌。为了产生成功的计算机辅助诊断系统，必须解决几个问题。分割是首先要考虑的问题，它有助于生成用于检测癌症结节的候选区域。第二个问题是从所有候选结节中识别受影响的结节。最初，将基本图像处理技术（例如位平面切片，侵蚀，中值过滤器，膨胀，轮廓，肺边界提取和洪水填充算法）应用于CT扫描图像，以检测肺区域。然后应用分割算法，以便从提取的肺部图像中检测出癌结节。本文采用模糊可能性C均值（FPCM）算法进行分割。分割后，将基于规则的技术应用于癌症结节的分类。最后，从提取的特征中生成一组诊断规则。根据这些规则，可以清楚地识别出癌症结节的发生。由于分类更好，因此可以在极限学习机（ELM）的帮助下进行学习。为了试验所提出的技术，从知名医院收集了CT图像。所提出的系统能够准确地检测假阳性结节。

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