Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

Abstract

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.

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“…Other than that, adding to many features into classifier would harm its performance. Some researchers had stated that finding meaningful patterns is more challenging when there Lung cancer classification [17] Local binary pattern (LBP) based features Nodule vs non-nodule [18] Gabor features Lung cancer diagnosis [19] Wavelet features, texture features and histogram features Lung cancer prediction [20] Super pixels features Tumour vs non-tumour vs fundus (segmentation) [21] Morphological features, genomic features, and molecular features Tumour vs non-tumour…”