Development of the FCM Method to Improve Clustering Accuracy in Big Data
Keywords:
Clustering, Optimization, FCM, Fuzzy Logic, Big Data, Medical ImagesAbstract
The objective of this study is to design a hybrid model based on Fuzzy C-Means (FCM) and Deep Learning in order to improve clustering accuracy in big data, particularly in the context of medical imaging. In this study, with the goal of enhancing clustering accuracy for skin lesion diagnosis, dermoscopic images were first collected and analyzed using Convolutional Neural Networks (CNN). Then, the FCM algorithm was combined with deep learning and the LCAOA optimization algorithm to optimize cluster centers. Fuzzy logic was also integrated into the system to improve the fitness function of the clustering algorithm. A hybrid method combining FCM, LCAOA, deep learning, and fuzzy logic was proposed. This approach improves clustering accuracy by refining the fitness function and optimizing cluster centers. The method was evaluated on medical images of skin cancer (melanoma) and showed significantly better performance and accuracy in automatic melanoma detection compared to conventional algorithms.
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Copyright (c) 2024 Hadis Changizi (Author); Mohammad Ali Afshar Kazemi (Corresponding Author); Alireza Pourebrahimi, Reza Radfar (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
