Classification of Normal versus Leukemic Cells with Data Augmentation and Convolutional Neural Networks

Abstract

Acute lymphoblastic leukemia is the most common childhood leukemia. It is an aggressive cancer type and causes various health problems. Diagnosis depends on manual microscopic analysis of blood samples by expert hematologists and pathologists. To assist these professionals, image processing and pattern recognition techniques can be used. This work proposes simple modifications to standard neural network architectures to achieve high performance in the malignant leukocyte classification problem. The tested architectures were VGG16, VGG19 and Xception. Data augmentation was employed to balance the Training and Validation sets. Transformations such as mirroring, rotation, blurring, shearing, and addition of salt and pepper noise were used. The proposed method achieved an F1-score of 92.60%, the highest one when compared to other participants’ published results and eighth position when compared to the weighted F1-score provided by the competition leaderboard.