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Artificial Bee Colony (ABC) optimization Algorithm based Automatic Segmentation and Detection of Suspicious Lesions in Lung CT Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 41 |
| Year of Publication: 2024 |
| Authors: Divya A., Janaki Sathya D. |
10.5120/ijca2024924018
|
Divya A., Janaki Sathya D. . Artificial Bee Colony (ABC) optimization Algorithm based Automatic Segmentation and Detection of Suspicious Lesions in Lung CT Images. International Journal of Computer Applications. 186, 41 ( Sep 2024), 7-13. DOI=10.5120/ijca2024924018
Abstract
With the increasing reporting cases of lung cancer there is an increasing demand for the detecting of the tumor at the initial state. With various computer aided algorithmized detection schemes doing a better job in the detection, the accuracy of these detection schemes could be always improved by introducing the newer optimization algorithms. The Artificial Bee Colony (ABC) optimisation algorithm is a novel optimisation technique that proceeds with the assumption of the existence of operations that resembles the biological behaviours of the honey bee in searching for food. For instance, each solution represents the food source locations and the bees are involved in finding the best solution. The fitness value, strongly linked to the solution, refers to the quality of the solution. With this optimisation algorithm the threshold levels are determined which then segments the various pixels into clusters thereby as a result the tumour region is correctly segmented with a better accuracy than the other algorithms. The artificial bee colony algorithm demonstrates robustness to image variability, evidenced by its high accuracy of 97.94%. Additionally, it provides detailed visualization of the shape of abnormal tissue around the lesion area.
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