Background and Objective

This research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity. The most significant level of disease for highly metastatic cancer cells are the degree of disturbance, contrast as well as entropy refers to the energy and homogeneity. A texture classification scheme to quantify the emphysema in Computed Tomography (CT) pictures is performed. Local binary models (LBP) are used to characterize areas of concern as texture characteristics and intensity histograms. A wavelet filter is used to acquire the informative matrix of each picture and decrease the dimensionality of the function space in the suggested method. A four-layer profound creed network is also used to obtain characteristics of elevated stage. Local Tangent Space Alignment (LTSA) is then used to compress the multi-domain defect characteristics into low dimensional vectors as a dimension reduction method. An unmonitored deep-belief network (DBN) is intended for the second phase to learn the unmarked characteristics. The strategy suggested uses Opposition Based Teaching (OBL), Position Clamping (PC) and the Cauchy Mutation (CM) to improve the fundamental PBA efficiency.

Methods

This research presents a fresh meta-heuristic algorithm called Opposition-Based Pity Beetle Algorithm (OPBA), which assesses effectiveness against state-of-the-art algorithms. OBL speeds up the convergence of the technique as both PC and CM assist OPBA with escaping local optima. The suggested algorithm was motivated by the behaviour of the beetle, which had been named six-toothed spruce bark beetle to aggregate nests and meals. This beetle can be found and harvested from weakened trees ' bark in a forest, while its populace can also infest healthy and robust trees when it exceeds the specified threshold.

Results & Conclusion

The methodology has been evaluated on CT imagery from the Lung Image Database Consortium and Image Resources Initiative (LIDC-IDRI), with a maximum sensitivity of 96.86%, precision of 97.24%, and an accuracy of 97.92%.

中文翻译：

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基于对立可怜甲虫算法的最佳深信度网络，用于肺癌分类：一种DBNOPBA方法

背景与目的

这项研究提出了一种成功的方法，该方法可以提取灰度共现矩阵（GLCM）图片处理模型，以对具有五种常见癌细胞系的低和高转移性癌症生物进行分类，并结合扫描激光图片投影技术和典型纹理功能，即对比度，相关性，功效，温度和均匀性。对于高度转移性癌细胞，疾病的最显着水平是干扰程度，对比度以及熵是指能量和同质性。执行纹理分类方案以量化计算机断层扫描（CT）图片中的气肿。局部二进制模型（LBP）用于将关注区域表征为纹理特征和强度直方图。小波滤波器用于获取每张图片的信息矩阵，并在建议的方法中减小函数空间的维数。四层深刻的信条网络也被用来获得提升阶段的特征。然后使用局部切线空间对齐（LTSA）将多域缺陷特征压缩为低维向量，以此作为降维方法。第二阶段旨在使用不受监视的深层信任网络（DBN），以学习未标记的特征。建议的策略使用基于对立的教学（OBL），位置钳制（PC）和柯西突变（CM）来提高基本的PBA效率。然后使用局部切线空间对齐（LTSA）将多域缺陷特征压缩为低维向量，以此作为降维方法。第二阶段旨在使用不受监视的深层信任网络（DBN），以学习未标记的特征。建议的策略使用基于对立的教学（OBL），位置钳制（PC）和柯西突变（CM）来提高基本的PBA效率。然后使用局部切线空间对齐（LTSA）将多域缺陷特征压缩为低维向量，以此作为降维方法。第二阶段旨在使用不受监视的深层信任网络（DBN），以学习未标记的特征。建议的策略使用基于对立的教学（OBL），位置钳制（PC）和柯西突变（CM）来提高基本的PBA效率。

方法

这项研究提出了一种新的元启发式算法，称为基于对立的可怜的甲虫算法（OPBA），该算法可评估针对最新算法的有效性。OPC加快了技术的收敛速度，因为PC和CM都通过避免局部最优来辅助OPBA。所建议的算法是由甲虫的行为所激发的，该甲虫被称为六齿云杉树皮甲虫，用于聚集巢和食物。这种甲虫可以在森林中被削弱的树皮中找到并收获，而其甲虫在超过规定阈值时也可以侵害健康而健壮的树木。

结果与结论

该方法已在肺图像数据库协会和图像资源计划（LIDC-IDRI）的CT图像上进行了评估，其最高灵敏度为96.86％，精度为97.24％，准确性为97.92％。