基于VGG-16卷积神经网络架构的白血病和类白血病分类

Q4 Biochemistry, Genetics and Molecular Biology
G. Sriram, T. R. Ganesh Babu, R. Praveena, J. V. Anand
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引用次数: 6

摘要

类白血病反应,如白血病,表明白细胞计数明显增加,但其原因是由于身体其他部位的严重炎症或感染。在白血病和类白血病反应分类的自动诊断中,使用了ALL IDB2 (Acute Lymphoblastic leukemia - image database)数据集,该数据集包含110张原始细胞和健康细胞的训练图像。本文旨在利用机器学习从血液涂片图像中自动区分白血病和白血病样反应。最初,自动检测和计数白细胞是为了确定白细胞增多,然后自动检测白细胞母细胞,以支持白血病和类白血病反应的分类。白细胞增多症在白血病和类白血病中都很常见,因此对于有类白血病反应的患者,医生可能有误诊为恶性白血病的机会。使用BCCD(血细胞计数检测)数据集,该数据集有364张血液涂片图像,其中349张为单一白细胞类型。应用了基于分水岭的色相饱和度值图像分割算法。VGG16 (Visual Geometric Group)基于卷积神经网络(CNN)架构的深度学习技术被用于从分割图像中分类和计数WBC类型。使用基于VGG16架构的CNN进行分类,并对第一部分得到的分割图像进行测试,对WBC爆炸进行识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.
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来源期刊
Molecular & Cellular Biomechanics
Molecular & Cellular Biomechanics CELL BIOLOGYENGINEERING, BIOMEDICAL&-ENGINEERING, BIOMEDICAL
CiteScore
1.70
自引率
0.00%
发文量
21
期刊介绍: The field of biomechanics concerns with motion, deformation, and forces in biological systems. With the explosive progress in molecular biology, genomic engineering, bioimaging, and nanotechnology, there will be an ever-increasing generation of knowledge and information concerning the mechanobiology of genes, proteins, cells, tissues, and organs. Such information will bring new diagnostic tools, new therapeutic approaches, and new knowledge on ourselves and our interactions with our environment. It becomes apparent that biomechanics focusing on molecules, cells as well as tissues and organs is an important aspect of modern biomedical sciences. The aims of this journal are to facilitate the studies of the mechanics of biomolecules (including proteins, genes, cytoskeletons, etc.), cells (and their interactions with extracellular matrix), tissues and organs, the development of relevant advanced mathematical methods, and the discovery of biological secrets. As science concerns only with relative truth, we seek ideas that are state-of-the-art, which may be controversial, but stimulate and promote new ideas, new techniques, and new applications.
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