Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction

IF 1 Q4 OPTICS
Faiyaz Ahmad, U. Hariharan, S. Karthick, Vaibhav Eknath Pawar,  S. Sharon Priya
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引用次数: 1

Abstract

Lung cancer is one of the most complicated diseases in human assessment. It affects the lives of humans critically. For the treatment of the lungs and prevention of the disease, it is important for an accurate and timely diagnosis of the disease. In most aspects, it was not as successful as the traditional method, which uses past medical history. Classification techniques are effective and reliable in categorizing affected lungs and people with normal lungs. But unfortunately failed to provide proper nodule location as this became a tedious task to find it. In the proposed method, Hybrid Particle Swarm Optimization-Lung Nodule Candidate (HPSO-LNDC) Detection depends on the diagnostic system using disease image data set for lung segmentation assessment. Initially, the input Data images are pre-processed to reduce the computational complexity of the system. Then, those segmented images are subject to the proposed HPSO-LNDC model. To solve the problem of accuracy, so many researchers have used slicing techniques, but most of these techniques are stuck in the local minima and suffer from premature convergence. Therefore the HPSO-LNDC model is integrated with the Hybrid PSO to reduce loss function occurring in the lung nodule detection architecture. Such a combination helps the HPSO-LNDC to avoid low similarity values. The proposed algorithm is simulated using the MATLAB tool and tested experimentally. It is defined as accuracy, detection level and Dice Similarity Coefficient (DSC). Results show that HPSO-LNDC with 85% accuracy and DSC of 90% was better than conventional methods.

Abstract Image

基于轮廓特征提取的肺癌肺结节预测模型优化
肺癌是人类评估中最复杂的疾病之一。它严重影响着人类的生活。对于肺的治疗和疾病的预防,准确及时地诊断疾病是很重要的。在大多数方面,它不像传统方法那样成功,传统方法使用过去的病史。分类技术是有效和可靠的分类受影响的肺和正常肺的人。但不幸的是,未能提供适当的结节位置,因为这成为一个繁琐的任务,找到它。在该方法中,混合粒子群优化-肺结节候选(HPSO-LNDC)检测依赖于使用疾病图像数据集进行肺分割评估的诊断系统。首先,对输入的数据图像进行预处理,以降低系统的计算复杂度。然后,将这些分割后的图像应用于所提出的HPSO-LNDC模型。为了解决精度问题,许多研究者使用了切片技术,但这些技术大多停留在局部极小值,存在过早收敛的问题。因此,将HPSO-LNDC模型与混合粒子群算法相结合,减少肺结节检测体系结构中出现的损失函数。这样的组合有助于HPSO-LNDC避免低相似性值。利用MATLAB工具对该算法进行了仿真,并进行了实验验证。其定义为准确率、检测水平和骰子相似系数(DSC)。结果表明,HPSO-LNDC的准确度为85%,DSC为90%,优于常规方法。
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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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