Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection
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S.I.: DEEP NEURO-FUZZY ANALYTICS IN SMART ECOSYSTEMS
Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection Surbhi Vijh1 • Prashant Gaurav2 • Hari Mohan Pandey3 Received: 24 July 2020 / Accepted: 9 September 2020 The Author(s) 2020
Abstract In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. Keywords Medical imaging Artificial intelligence Feature extraction Hybrid WOA_APSO Convolutional neural network
1 Introduction
& Hari Mohan Pandey [email protected] Surbhi Vijh [email protected] Prashant Gaurav [email protected] 1
Department of Information Technology, KIET Group of Institution, Ghaziabad, India
2
Department of Computer Science and Engineering, Amity University, Sector 125, Noida, Uttar Pradesh, India
3
Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, England, UK
According to WHO statistics, the estimated worldwide death of people from cancer is 9.6 million in 2018. However, it is observed that 30–50% of people can be prevented from cancer by providing preventive measures and treatment at an early stage of cancer. Lung cancer is also referred to as lung carcinoma characterized by uncontrollable cell growth in tissues which generally hav
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