A Construction of Object Detection Model for Acute Myeloid Leukemia  

作　　者：K.Venkatesh S.Pasupathy S.P.Raja 

机构地区：[1]Department of Computer Science and Engineering,Annamalai University,Chidambaram,608002,India [2]School of Computer Science and Engineering,Vellore Institute of Technology,Vellore,632014,India

出　　处：《Intelligent Automation & Soft Computing》2023年第4期543-560,共18页智能自动化与软计算（英文）

摘　　要：The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)prediction.It takes an enormous number of times to ana-lyze with the standardization and inter-observer variability.Here,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological Dataset.The proposed Faster R-CNN model is trained using the augmented dataset.For overcoming the Imbalanced Data problem,data augmentation techniques are imposed.The Faster R-CNN performance was com-pared with existing transfer learning techniques.The results show that the Faster R-CNN performance was significant than other techniques.The number of images in each class is different.For example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven images.The dataset is used to train the CNN for single-cell morphology classification.The proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关 键 词：Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu 

分 类 号：R733.7[医药卫生—肿瘤] TP39[医药卫生—临床医学]