IMPROVED DUCK AND TRAVELER OPTIMIZATION (IDTO) ALGORITHM: A TWO- WAY EFFICIENT APPROACH FOR BREAST TUMOR SEGMENTATION USING MULTILEVEL THRESHOLDING
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 10, Pages 3793-3808
Abstract
The merely human being who can accumulate yourself is you only. The pathologist is the caretaker who can help you to protect yourself from breast cancer. Invasive breast cancer of US women is approximately 281,550 in 2021. Objective: Early diagnosis of breast cancer using computer aided system is needed to create novel methods for fighting against the diseases. Methods: In this research a new Meta heuristic Duck Traveler Optimization algorithm is introduced for mammogram image segmentation. Automatic selection of threshold values is help to optimize the segmentation process in an efficient manner. IDTO calculation is utilized to amplify the Kapur's and Otsu's goal capacities. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) of swarm intelligence algorithms are efficiently used for validating the performance of IDTO. Datasets: MIAS Dataset consists of 322 mammogram images are taken for experimentation. Findings: The results of PSNR values proved that the proposed IDTO leads the optimum threshold values better than PSO and GA. The execution season of IDTO division technique is assessed and contrasted with GSA. The best average time of IDTO has been declared the proposed method has the high efficiency with minimum time.
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