A Fast and Light Weight Deep Convolution Neural Network Model for Cancer Disease Identification in Human Lung(s)

Abstract

In the proposed work, a convolution neural network (CNN) based model has been used to identify the cancer disease in human lung(s). Moreover, this approach identifies the single or multi-module in lungs by analyzing the Computer Tomography (CT) scan. For the purpose of the experiment, publicly available dataset named as Early Lung Cancer Action Program (ELCAP) has been used. Moreover, the performance of proposed CNN model has been compared with traditional machine learning approaches i.e. support vector machine, k-NN, Decision Tree, Random Forest, etc under various parameters i.e. accuracy, precision, recall, Cohen Kappa. The performance of proposed model is also compared with famous CNN models i.e. VGG16, Inception V3 in terms of accuracy, storage space and inference time. The experimental results show the efficacy of proposed algorithms over traditional machine learning and pre-trained models by achieving the accuracy of 99.5%