scholarly journals An accurate algorithm to match imperfectly matched images for lung tumor detection without markers

2015 ◽  
Vol 16 (3) ◽  
pp. 131-140 ◽  
Author(s):  
Timothy Rozario ◽  
Sergey Bereg ◽  
Yulong Yan ◽  
Tsuicheng Chiu ◽  
Honghuan Liu ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Detection

SU-E-J-23: An Accurate Algorithm to Match Imperfectly Matched Images for Lung Tumor Detection Without Markers

2014 ◽  
Vol 41 (6Part6) ◽  
pp. 159-159
Author(s):  
T Rozario ◽  
S Bereg ◽  
T Chiu ◽  
H Liu ◽  
V Kearney ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Detection

scholarly journals Automatic Lung Tumor Detection Based on GLCM Features

2015 ◽  
pp. 109-121 ◽  
Author(s):  
Mir Rayat Imtiaz Hossain ◽  
Imran Ahmed ◽  
Md. Hasanul Kabir
Keyword(s):  
Lung Tumor ◽  
Tumor Detection

scholarly journals Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

2020 ◽  
Author(s):  
Surbhi Vijh ◽  
Prashant Gaurav ◽  
Hari Mohan Pandey
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lung Tumor ◽  
Tumor Detection ◽  
Whale Optimization Algorithm ◽  
Swarm Optimization ◽  
Whale Optimization

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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