Lung Cancer Nodules Detection Using Ideal Features Extraction Technique in CT Images

DOI

10.2991/978-94-6463-196-8_5How to use a DOI?

Keywords

Lung Cancer Nodules; CT Images; CAD; Pre-processing; Segmentation; Feature Extraction; Classification

Abstract

In the present time, worldwide the number of patients related to lung cancer disease getting increase exponentially, accordingly the application of Computer Aided Diagnosis (CAD) system building association with medical science to deliver pertinent solution using Image Processing and Machine Learning Techniques. This paper presenting a model for Lung Cancer nodules detection in CT image by employing proposed work in the progressive phases, the first step is image pre-processing which uses standard LIDC-IDRI images as input images, the pre-processing approach employ the denoising technique to remove the speckle noises from images, then by applying adaptive contrast enhancement (CLAhe) technique the quality of input CT image is improved. The second step works on to segment the Region of Interest (ROI) using LevelSet segmentation algorithm, the third step employed the learnable Feature Extraction technique from suspected (ROI) in CT image, the feature to be extracted from CT images are Texture Features such as Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Run-Length Matrix (GLRM), Local Binary Pattern (LBP), Shape-Based Features such as Perimeter, Area, Irregularity Index, Solidity, Equivalent Diameter, Convex Area, and Statistical Features such as Mean, Median, Mode, Entropy, Moment, Skewness, and Kurtosis. The optimized measurable features are offered as input to the Hybrid-Layer Convolutional Neural Network, Hybrid-CNN applied Enhanced Cat-Swarm Optimization algorithm for optimal weight selection. The convolutional neural network trained based on feature values by varying the training percentage of dataset (in the range of 50%, 60%, 70%, 80%, and 90%) and the proposed model attains the elevated accuracy of 92.93%. The performance evaluation metrics are used such as Sensitivity, Specificity, Accuracy and F-Measure to evaluate the robustness of proposed Hybrid-CNN classification model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vikul J. Pawar
AU  - P. Premchand
AU  - I. Govardhanrao
PY  - 2023
DA  - 2023/08/10
TI  - Lung Cancer Nodules Detection Using Ideal Features Extraction Technique in CT Images
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 39
EP  - 57
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_5
DO  - 10.2991/978-94-6463-196-8_5
ID  - Pawar2023
ER  -