Improved Salp Swarm Optimization-based Fuzzy Centroid Region Growing for Liver Tumor Segmentation and Deep Learning Oriented Classification


Ramchand Hablani
Suraj Patil
Dnyaneshwar Kirange


Due to heterogenous shape of liver, the segmentation and classification of liver is challenging task. Therefore, Computer-Aided Diagnosis (CAD) is employed for predictive decision making for liver diagnosis. The major intuition of this paper is to detect liver cancer in a precise manner by automatic approach. The developed model initially collects the standard benchmark LiTS dataset, and image preprocessing is done by three techniques like Histogram equalization for contrast enhancement, and median filtering and Anisotropic diffusion filtering for noise removal. Further, the Adaptive thresholding is adopted to perform the liver segmentation. As a novelty, optimized Fuzzy centroid-based region growing model is proposed for tumor segmentation in liver. The main objective of this
tumor segmentation model is to maximize the entropy by optimizing the fuzzy centroid and threshold of region growing using Mean Fitness-based Salp Swarm Optimization Algorithm (MF-SSA). From segmented tumor, the features like Local Directional Pattern (LDP) and Gray Level Co-occurrence Matrix (GLCM) are extracted. The extracted features are given as input to NN, and segmented tumor is given to Convolutional Neural Network (CNN). The AND bit operation to both of the outputs obtained from NN and CNN confirms the healthy and unhealthy CT images. Since the number of hidden neurons makes an effect on final classification output, the optimization of neurons is done using MF-SSA. From the experimental analysis, it is confirmed that the proposed model is better as compared with state of art results of previous study can assist radiologists in tumor diagnosis from CT scan images.


How to Cite
Ramchand Hablani, Suraj Patil, & Dnyaneshwar Kirange. (2022). Improved Salp Swarm Optimization-based Fuzzy Centroid Region Growing for Liver Tumor Segmentation and Deep Learning Oriented Classification. International Journal of Next-Generation Computing, 13(5).


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