Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning

A. Genovese, M. S. Hosseini, V. Piuri, K. Plataniotis, F. Scotti
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引用次数: 27

Abstract

Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging fields, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has specifically analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classification. To address this issue, in this paper we propose the first machine learning-based approach able to enhance blood sample images by an adaptive unsharpening method. The method uses image processing techniques and DL to normalize the radius of the cell, estimate the focus quality, adaptively improve the sharpness of the images, and then perform the classification. We evaluated the methodology on a public database of ALL images, considering several state-of-the-art CNNs to perform the classification, with results showing the validity of the proposed approach. For a complete reproducibility of the work, the source code is available at: http://iebil.di.unimi.it/cnnALL/index.htm.
基于自适应非锐化和深度学习的急性淋巴细胞白血病检测
计算机辅助诊断(CAD)系统越来越多地利用图像分析和深度学习(DL)技术,因为它们在几个医学成像领域具有很高的准确性,包括从外周血样本中检测急性淋巴母细胞(或淋巴细胞)白血病(ALL)。然而,文献中没有一种方法专门分析ALL图像的焦点质量,或者提出一种自适应锐化样本的技术来进行分类。为了解决这个问题,在本文中,我们提出了第一种基于机器学习的方法,能够通过自适应非锐化方法增强血液样本图像。该方法利用图像处理技术和深度学习技术对单元半径进行归一化,估计焦点质量,自适应提高图像的清晰度,然后进行分类。我们在所有图像的公共数据库上评估了该方法,考虑了几个最先进的cnn来执行分类,结果显示了所提出方法的有效性。对于一个完整的再现工作,源代码可在:http://iebil.di.unimi.it/cnnALL/index.htm。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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