Appadurai Jothi Prabha, G Suganeshwari, Prabhu Kavin Balasubramanian, C Kavitha, Lai Wen-Cheng
Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India.
Biomedicines. 2023 Feb 23;11(3):679. doi: 10.3390/biomedicines11030679.
In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.
近年来，肺癌预测是降低人类死亡率的一个重要课题。在文献部分，回顾了一些在预测阶段降低准确率的论文。因此，在本文中，我们开发了一种用于肺癌预测的多进程吸盘鱼优化卷积神经网络超参数（MPROH-CNN）。所提出的技术可用于检测人类肺部的CT图像。所提出的技术包括四个阶段，即预处理、特征提取和分类。首先，从开源系统收集数据库。之后，收集到的CT图像包含不需要的噪声，这会影响分类效率。因此，可以考虑采用预处理技术从输入图像中去除不需要的噪声，如滤波和对比度增强。接下来，借助直方图、纹理和小波等特征提取技术提取关键特征。提取的特征用于分类阶段。所提出的分类器是吸盘鱼优化算法（ROA）和卷积神经网络（CNN）的结合。在CNN中，ROA用于多进程优化，如结构优化和超参数优化。所提出的方法在MATLAB中实现，并通过使用准确率、精确率、召回率、特异性、灵敏度和F值等性能指标来评估性能。为了验证所提出的方法，分别将其与传统技术CNN、CNN-粒子群优化算法（PSO）和CNN-萤火虫算法（FA）进行比较。通过分析，所提出的方法在肺癌预测中达到了0.98的准确率水平。
