Invited Speech 1: A New Model To Classify The Acute Lymphoblastic Leukemia Image
Arif Muntasa · 2019
Acute lymphoblastic leukemia is a type of cancer which attacks blood cells and spinal cord. A patient needs to conduct early detection to avoid more severe conditions. In this research, we proposed a new model to detect acute lymphoblastic leukemia by microscopic images. We divide fives stages to classify acute lymphoblastic leukemia, i.e. An image enhancement, an image segmentation, noise removal, feature extraction, and classification. Firstly, we enhanced the green channel before the segmentation process. Secondly, we employed the entropy method to obtain the best threshold value. However, the segmentation results have delivered small regions besides the main object. Thirdly, we have to remove the small objects before feature extraction. In addition, We extract the main object using energy, entropy, shanon entropy, and the object circularity. Lastly, we modified a learning vector quantization. The proposed algorithm synchronized the weight value, which has the maximum probability. We have evaluated our proposed algorithm using the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2). Our proposed method has delivered 96.15% maximum accuracy. It proved that the proposed method has outperformed to the other methods, i.e. leukemia detection using Fuzzy, perceptron, and support vector machine