Automatic Lung Tumor Detection Based on GLCM Features

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.

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A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2019.8683802 ◽

2019 ◽

Cited By ~ 4

Author(s):

Shahruk Hossain ◽

Suhail Najeeb ◽

Asif Shahriyar ◽

Zaowad R. Abdullah ◽

M. Ariful Haque

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Lung Tumor ◽

Tumor Detection ◽

Ct Scans

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Review article of microwave imaging techniques and dielectric properties for lung tumor detection

10.1063/1.5142104 ◽

2020 ◽

Author(s):

Siti Nuranis Muhammad ◽

Muammar Mohamad Isa ◽

Faizal Jamlos

Keyword(s):

Dielectric Properties ◽

Lung Tumor ◽

Imaging Techniques ◽

Tumor Detection ◽

Review Article ◽

Microwave Imaging

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