Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause it is due to severe inflammation or infections in other body regions. In automatic diagnosis classifying and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images blast cells healthy cells. This paper aimed at an process distinguish reactions from blood smear using Machine Learning. Initially, detection counting WBC done identify leukocytosis then blasts performed support classification reactions. Leukocytosis commonly observed both hence physicians may have chance wrong malignant for patients with BCCD (blood cell detection) Dataset 364 349 are single type. The Image segmentation algorithm Hue Saturation Value color based on watershed applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture deep learning technique being incorporated type segmented images. obtained first part were tested blasts.