Automatic Contrast Enhancement with Differential Evolution for Leukemia Cell Identification
Image enhancement techniques are needed to decrease the negative effects of blur or unwanted noise in image processing. In biomedical images, the quality of images is very important to achieve an adequate identification to detection or diagnosis purposes. This paper addresses the use of contrast enhancement to facilitate the identification of leukemia in blood cell images. Differential evolution algorithm is used to get parameters required to apply contrast enhancement specifically in the interest region in the image, which facilites the posterior identification of leukemic cells. Identification of leukemic cells is accomplished applying an edges extraction and dilatation. From this image, two types of neural networks are used to classify the cells like healthy or leukemic cells. In first experiment, a multilayer perceptron is trained with the backpropagation algorithm using geometric features extracted from image. While in the second, convolutional networks are used. A public dataset of 260 healthy and leukemic cell images, 130 for each type, is used. The proposed contrast enhancement technique shows satisfactory results when obtaining the interest region, facilitating the identification of leukemic cells without additional processing, like image segmentation.
This way, computational resources are decreased. On the other hand, to identify the cell type, images are classified using neural networks achieving an average classification accuracy of \(99.83\%\).
KeywordsContrast enhancement Differential evolution Leukemia cells
This research was economically supported in part by the Instituto Politécnico Nacional, Mexico under projects SIP 20190007 and CONACYT 65 (Fronteras de la Ciencia); and in part by the Autonomous University of Tlaxcala, Mexico. R. Ochoa acknowledges CONACYT for the scholarship granted towards pursuing his PhD studies.
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