Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction
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 Co...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/3/679 |
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| author | Jothi Prabha Appadurai Suganeshwari G Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai |
| author_facet | Jothi Prabha Appadurai Suganeshwari G Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai |
| author_sort | Jothi Prabha Appadurai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:55:01Z |
| format | Article |
| id | doaj.art-ee7448e1e9f64fd195c8f640b0176c53 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T06:55:01Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-ee7448e1e9f64fd195c8f640b0176c532023-11-17T09:43:59ZengMDPI AGBiomedicines2227-90592023-02-0111367910.3390/biomedicines11030679Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer PredictionJothi Prabha Appadurai0Suganeshwari G1Balasubramanian Prabhu Kavin2Kavitha C3Wen-Cheng Lai4Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, IndiaDepartment of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Chengalpattu District, Chennai 603203, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, IndiaBachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, TaiwanIn 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.https://www.mdpi.com/2227-9059/11/3/679lung cancer predictionremora optimization algorithmCT imageperformance matricesfeature extraction and pre-processing |
| spellingShingle | Jothi Prabha Appadurai Suganeshwari G Balasubramanian Prabhu Kavin Kavitha C Wen-Cheng Lai Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction Biomedicines lung cancer prediction remora optimization algorithm CT image performance matrices feature extraction and pre-processing |
| title | Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction |
| title_full | Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction |
| title_fullStr | Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction |
| title_full_unstemmed | Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction |
| title_short | Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction |
| title_sort | multi process remora enhanced hyperparameters of convolutional neural network for lung cancer prediction |
| topic | lung cancer prediction remora optimization algorithm CT image performance matrices feature extraction and pre-processing |
| url | https://www.mdpi.com/2227-9059/11/3/679 |
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