• S. Bhavana Seshadri
• Dr. P.Harish
• Dr. N.Pushpalatha
• Dr. I.Suneetha

Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. Deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, deployed pre-trained Online AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. For acute lymphoblastic leukemia detection, achieved an acute lymphoblastic leukemia subtype classification the Accuracy was 96%, and specificity was 92.85%, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

MSVM, DCNN, Leukemia, Accuracy, Sensitivity, Specificity etc.

"Detection and Classification of Leukemia using DCNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.e617-e624, August-2022, Available :http://www.jetir.org/papers/JETIR2208473.pdf

"Detection and Classification of Leukemia using DCNN Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppe617-e624, August-2022, Available at : http://www.jetir.org/papers/JETIR2208473.pdf