Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image

Abstract

In this paper, the Lung Cancer Classification using Convolutional Neural Network with DenseNet-201 Transfer Learning model optimized through Namib Beetle Optimization Algorithm on CT image (AtCNN-DenseNet-201 TL-NBOA-CT) is proposed. The input data are gathered from the CT Lung image. In pre-processing section, it removes the noise and enhances input images by Modified Sage-Husa Kalman Filtering (MSHKF).The pre-processed image is given into feature extraction phase. Then, six statistical features including mean, variance, entropy, energy, Average Amplitude, kurtosis are extracted based on Improved Empirical Wavelet Transforms (IEWT). Then, the extracted features are given to the Attention-based Convolutional Neural Network with DenseNet-201Transfer Learning (AtCNN-DenseNet-201 TL) to classify the Cancer and Non-Cancer of the CT image, where batch normalization layer of At CNN eliminated and added by DenseNet-201 layer. Namib Beetle Optimization Algorithm (NBOA) proposed in this work to enhance the AtCNN-DenseNet-201 classifier, which precisely classifies the lung cancer. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method is implemented and the effectiveness is assessed with some performance measures. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method attains 18.30 %, 21.37 % and 23.07 % greater precision, 24.84 %, 16.32 % and 31.36 % greater accuracy and 14.32 %, 21.97 % and 23.38 % greater F1-Score compared with existing techniques like detection and categorization of lung cancer CT pictures utilizing improved deep belief network along Gabor filters (CLC-CT-EDBN), presented categorization of lung cancer CT pictures utilizing a three dimensional deep CNN with multi-layer filter (CLC-CT-3D-DCNN), and novel hybrid deep learning technique for early identification of lung cancer utilizing neural networks (CLC-CT-3D-CNN) respectively.