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. 2019 Aug 25;9(3):104.
doi: 10.3390/diagnostics9030104.

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Nizar Ahmed  1 , Altug Yigit  1 , Zerrin Isik  1 , Adil Alpkocak  2
Affiliations

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Nizar Ahmed et al. Diagnostics (Basel). .

Abstract

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

Keywords: convolutional neural network; data augmentation; deep learning; leukemia diagnosis; microscopic blood cells images; multi-class classification; recognizing leukemia subtypes.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Sample images of four different types of Leukemia: Chronic Lymphocytic Leukemia (CLL) [8], Chronic Myeloid Leukemia (CML) [8], Acute Lymphocytic Leukemia (ALL) [8], Acute Myeloid Leukemia (AML) [8], and HEALTHY [5].
Figure 2
Figure 2
The effect of applying image transformation on one image sample. (a) Original image, (b) rotation, (c) height shift, (d) width shift, (e) zoom, (f) horizontal flip, (g) vertical flip, (h) shearing.
Figure 3
Figure 3
The proposed convolutional neural network (CNN) architecture.
Figure 4
Figure 4
Comparison of our proposals with different machine learning algorithms for the multi-classification of all leukemia subtypes.
See this image and copyright information in PMC

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