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. 2024 Feb 27:10:e1802.
doi: 10.7717/peerj-cs.1802. eCollection 2024.

An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images

Maheswari Sivakumar  1 , Sundar Chinnasamy  2 , Thanabal Ms  3
Affiliations

An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images

Maheswari Sivakumar et al. PeerJ Comput Sci. .

Abstract

Background and objective: One of the illnesses with most significant mortality and morbidity rates worldwide is lung cancer. From CT images, automatic lung tumor segmentation is significantly essential. However, segmentation has several difficulties, such as different sizes, variable shapes, and complex surrounding tissues. Therefore, a novel enhanced combined intelligent system is presented to predict lung cancer in this research.

Methods: Non-small cell lung cancer should be recognized for detecting lung cancer. In the pre-processing stage, the noise in the CT images is eliminated by using an average filter and adaptive median filter, and histogram equalization is used to enhance the filtered images to enhance the lung image quality in the proposed model. The adapted deep belief network (ADBN) is used to segment the affected region with the help of network layers from the noise-removed lung CT image. Two cascaded RBMs are used for the segmentation process in the structure of ADBN, including Bernoulli-Bernoulli (BB) and Gaussian-Bernoulli (GB), and then relevant significant features are extracted. The hybrid spiral optimization intelligent-generalized rough set (SOI-GRS) approach is used to select compelling features of the CT image. Then, an optimized light gradient boosting machine (LightGBM) model using the Ensemble Harris hawk optimization (EHHO) algorithm is used for lung cancer classification.

Results: LUNA 16, the Kaggle Data Science Bowl (KDSB), the Cancer Imaging Archive (CIA), and local datasets are used to train and test the proposed approach. Python and several well-known modules, including TensorFlow and Scikit-Learn, are used for the extensive experiment analysis. The proposed research accurately spot people with lung cancer according to the results. The method produced the least classification error possible while maintaining 99.87% accuracy.

Conclusion: The integrated intelligent system (ADBN-Optimized LightGBM) gives the best results among all input prediction models, taking performance criteria into account and boosting the system's effectiveness, hence enabling better lung cancer patient diagnosis by physicians and radiologists.

Keywords: Adapted deep belief network; Adaptive median filter; Ensemble Harris hawk optimization; Histogram equalization; Light GBM.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. The proposed combined intelligent system (ADBN-optimized LightGBM) based lung cancer detection system structure.
Figure 2
Figure 2. (A–C) Phases of pre-processing.
Figure 3
Figure 3. Results for segmentation: (A) lung image, (B) ground truth, and (C) segmented image.
Figure 4
Figure 4. Dataset distribution.
Figure 5
Figure 5. Accuracy of training and validation for the proposed lung cancer classification system.
Figure 6
Figure 6. Training loss and validation loss for the proposed lung cancer classification system.
Figure 7
Figure 7. ROC curve attained for proposed lung cancer classification system.
See this image and copyright information in PMC

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