Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification
Anju Bala1, Priti2
1Anju Bala, Research Scholar, Department of Computer Science and Applications, M.D.U, Rohtak, India.
2Priti, Assistant Professor, Department of Computer Science and Applications, M.D.U, Rohtak, India.
Manuscript received on 16 March 2019 | Revised Manuscript received on 23 March 2019 | Manuscript published on 30 July 2019 | PP: 3944-3948 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2374078219/19©BEIESP | DOI: 10.35940/ijrte.B2374.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast Cancer is one of the fastest growing cancer that causes women to death in the world. The early detection of breast cancer improves the chances of its cure. The malignant tumor that is the sign of breast cancer can be detected by mammography. This paper develops a technique to classify the mammogram images as normal, benign or malignant. This paper applies HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm to texture features extracted from the mammogram image. The analysis has been done on the DDSM and MIAS dataset by using classification accuracy, specificity, and sensitivity as the parameter with three state of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization), ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization). The improvement in the performance measures against three state of art techniques shows the significance of the algorithm.
Index Terms: Mammogram, Classification Accuracy, Malignant tumor, ACO, PSO, GA, DE
Scope of the Article: Classification