Optimization of Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony (ABC) Algorithm for Classification of Bone Cancer

Bone cancer is the uneven progress of tissue in the bone. This illness can be primary or secondary. Primary bone sarcoma grows from the cells of the bone, while secondary bone cancer begins from different body organs and then extends to the bone cells. The cure of fillet cancer depends on tumor loca...

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Veröffentlicht in:2022 Second International Conference on Distributed Computing and High Performance Computing (DCHPC) S. 78 - 81
Hauptverfasser: Lefteh, Ali, Houshmand, Monireh, Khorrampanah, Mahsa, Smaisim, Ghassan Fadhil
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 02.03.2022
Schlagworte:
Adaptive systems
ANFIS
Artificial bee colony algorithm
Bone Cancer
Bones
Clustering algorithms
Feature extraction
Fuzzy C-mean Clustering
Inference algorithms
Meta-Heuristic Algorithms
MRI
Productivity
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Zusammenfassung:Bone cancer is the uneven progress of tissue in the bone. This illness can be primary or secondary. Primary bone sarcoma grows from the cells of the bone, while secondary bone cancer begins from different body organs and then extends to the bone cells. The cure of fillet cancer depends on tumor localization, size, and other factors. Therefore, initial recognition and category of bone cancer have become required to therapy the patient. In this procedure, a technique proposed for the glory of bone cancer employing fuzzy C-mean clustering also uses Modified Adaptive Neuro-Fuzzy Inference System (MANFIS) with Artificial Bee Colony algorithm to classify benign and malignant bone cancer. The experiments illustrate that the suggested method has a high accuracy of 96.67% and offers enhanced productivity than the systems in bone MR images.
DOI:10.1109/DCHPC55044.2022.9731840