Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

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.

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

“…Moreover, increasing the number of layers increases nonlinearities, which also decreases the weights, to be optimized, by 77% and 81% for the first and the second case respectively. In addition, the proposed architecture comprised of only one or two layers in depth to alleviate the problem of overfitting; this is in agreement with Ahmed et al [76] and RStudio online tutorials [79]. Though, ten-fold crossvalidation technique was incorporated to give a good estimate about the generalizability of the classification [80,81].…”

“…One of the biggest hurdles encountered when dealing with machine learning in general, and neural networks in specific, is the limitation of the dataset; especially when dealing with medical data; this is the main cause of overfitting [74]. The batch normalization layer is used to reduce the problem of overfitting [75,76] due to its importance in deep learning [77]. In addition, the usage of small-sized filters is usually enhancing the test set performance measures compared to larger filters as explained in the Methods section, through decreasing the overfitting which aligns with Pereira et al [78] who advocated that small filter sizes of 3×3 would minimize the effect of overfitting since the number of parameters to be learnt decreased.…”

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

“…The parameters' values are assigned as w low = 0.7, w bon = 0.3, and δ = 0.5. These values give possible stable results in rough k-means [30]. Figure 2 illustrates the segmentation results produced by the proposed HSCRKM algorithm.…”

“…In [30], the convolutional neural networks (CNN) approach is applied to identify the subtypes of leukemia. It is observed from the experimental results that the CNN model achieves 88.25% and 81.74% accuracy for leukemia and healthy cells, respectively.…”

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

“…[32][33][34] ML can use these techniques to analyze whole slides with automated focusing. 35 Classification of leukemia subtypes (AML, acute lymphoblastic leukemia, chronic myeloid leukemia, and chronic lymphocytic leukemia) can be achieved by a variety of ML approaches such as DNN, 36 SVM, and k-means-clustering (an unsupervised ML technique in which similar data points are grouped into k clusters according to their distance to a cluster mean). 37,38 Another essential part of the diagnostic process in AML is flow cytometry, 39 which can aid in the detection of relapse with a higher sensitivity than cytomorphology alone.…”