The early stages of the condition are notoriously difficult to diagnose due to the fact that abnormal cells with dimensions less than very small are notoriously difficult to spot by imaging. If identification occurs at an earlier stage, then there is a chance that the probability of extending the lifespan of the individual may increase. Nevertheless, because of the enormous dimensionality of the database space, timely diagnosis is a challenging task. In this paper, nodule segmentation is proposed using Enhanced Local Information Weighted Intuitionistic Fuzzy C-Means (ELWI-FCM) clustering. After pre-processing, segmentation is performed by ELWI-FCM. To optimize the performance of FCM, the improved Golden Eagle Optimization (IGEO) algorithm is used. For classifying the nodules as normal or affected, the pre-trained DenseNet201 model is utilized. Experiments are conducted over the LIDC-IDRI dataset. Experimental results show that the proposed ELWI-FCM-IGEO attains better accuracy, precision, F1-score, sensitivity, and specificity compared to existing models.
Keywords
Enhanced Local Information Weighted Intuitionistic, Fuzzy C-Means Clustering, Golden Eagle Optimization, DenseNet201.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Sandhya L and Marimuthu Karuppiah;
Methodology: Sandhya L;
Data Curation: Sandhya L;
Writing- Original Draft Preparation: Sandhya L;
Visualization: Sandhya L;
Supervision: Marimuthu Karuppiah;
Validation: Marimuthu Karuppiah;
Writing- Reviewing and Editing: Sandhya L and Marimuthu Karuppiah; All authors reviewed the results and approved the final version of the manuscript.
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Marimuthu Karuppiah
Presidency School of Computer Science and Engineering, Presidency University, Bengaluru, Karnataka, India.
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Cite this article
Sandhya L and Marimuthu Karuppiah, “Enhanced Local Information Weighted Intuitionistic Fuzzy C-Means Clustering for Image Nodule Segmentation”, Journal of Machine and Computing, vol.5, no.4, pp. 2373-2385, October 2025, doi: 10.53759/7669/jmc202505184.
