Acute Myelogenous Leukemia Detection using Circumventing Ant Colony Optimization based Convolutional Neural Network
B.Ramya1,V.Uma Rani2
1B.Ramya,Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore.
2Dr. V. Uma Rani, Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore. 

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 705-712 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6732098319/2019©BEIESP | DOI: 10.35940/ijrte.C6732.118419

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Abstract: Acute Myelogenous Leukemia (AML) is a type of disease associated with acute leukemia which is getting increased in both children’s and adults. AML falls under the category of cancer disease. The term acute in AML indicates rapid progression of disease in human body. The main challenge of medical field in vision of computer and multimedia is texture and color between various categories. The variation in texture and color attributes makes the classification task a tedious. Deep learning has shown its dazzling performance in various streams, which includes classification too. The objective of image classification is to differentiate the subcategories that belong to same basic-level category. The main objective of this paper is to propose bioinspired based on convolutional neural network to classify the microscopic blood images for AML. This paper has utilized bioinspired concept to extract the features more reliably. Bench mark performance metrics were chosen to evaluate the proposed classifier against the previous classifiers based on two parameters. The results indicate that the proposed classifiers has outperformed the previous works towards the classification of AML.
Keywords: AML, ACO, CNN, Classification, Deep Learning.
Scope of the Article: Deep Learning.