Assessing the impact of data augmentation and a combination of CNNs on leukemia classification

An accurate early-stage leukemia diagnosis plays a critical role in treating and saving patients' lives. The two primary forms of leukemia are acute and chronic leukemia, which is subdivided into myeloid and lymphoid leukemia. Deep learning models have been increasingly used in computer-aided medical diagnosis (CAD) systems developed to detect leukemia. This article assesses the impact of widely applied techniques, mainly data aug-mentation and multilevel and ensemble configurations, in deep learning-based CAD sys-tems. Our assessment included five scenarios: three binary classification problems and two multiclass classification problems. The evaluation was performed using 3,536 images from 18 datasets, and it was possible to conclude that data augmentation techniques improve the performance of convolutional neural networks (CNNs). Furthermore, there is an improvement in the classification results using a combination of CNNs. For the binary problems, the performance of the ensemble configuration was superior to that of the mul-tilevel configuration. However, the results were statistically similar in multiclass scenarios. The results were promising, with accuracies of 94.73% and 94.59% obtained using multi-level and ensemble configurations in a scenario with four classes. The combination of methods helps to reduce the error or variance of the predictions, which improves the accu-racy of the used deep learning-based model.(c) 2022 Published by Elsevier Inc.

Automated summary

This article evaluates the impact of widely applied techniques, including data augmentation and multilevel and ensemble configurations, in deep learning-based computer-aided diagnosis systems. The findings suggest that data augmentation techniques improve the performance of convolutional neural networks, and a combination of CNNs also enhances the classification results.