Next Article in Journal
The HD-OCT Study May Be Useful in Searching for Markers of Preclinical Stage of Diabetic Retinopathy in Patients with Type 1 Diabetes
Previous Article in Journal
Diagnostic Performance of a Support Vector Machine for Dermatofluoroscopic Melanoma Recognition: The Results of the Retrospective Clinical Study on 214 Pigmented Skin Lesions
Open AccessArticle

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(3), 104; https://doi.org/10.3390/diagnostics9030104
Received: 2 June 2019 / Revised: 22 August 2019 / Accepted: 23 August 2019 / Published: 25 August 2019
(This article belongs to the Section Medical Imaging)
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 multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms. View Full-Text
Keywords: leukemia diagnosis; recognizing leukemia subtypes; multi-class classification; microscopic blood cells images; data augmentation; deep learning; convolutional neural network leukemia diagnosis; recognizing leukemia subtypes; multi-class classification; microscopic blood cells images; data augmentation; deep learning; convolutional neural network
Show Figures

Figure 1

MDPI and ACS Style

Ahmed, N.; Yigit, A.; Isik, Z.; Alpkocak, A. Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network. Diagnostics 2019, 9, 104.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop