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Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

K. Jagadeesh1,*, A. Rajendran2

1 Department of Electronics and Communication Engineering, SNS College of Engineering, Coimbatore, Tamilnadu, India
2 Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamilnadu, India

* Corresponding Author: K. Jagadeesh. Email: email

Computer Systems Science and Engineering 2023, 45(2), 2017-2032.


Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called adaptive median filter and average filter. The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest) cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique. For classification of images, Probabilistic Neural Networks (PNN) based classification is used. The experimentation is carried out by simulating the model in MATLAB, with the input CT lung images LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) benchmark Dataset. The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.


Cite This Article

K. Jagadeesh and A. Rajendran, "Improved model for genetic algorithm-based accurate lung cancer segmentation and classification," Computer Systems Science and Engineering, vol. 45, no.2, pp. 2017–2032, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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