  Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by
numerous immature lymphocytes. Even though automation in ALL prognosis is an
essential aspect of cancer diagnosis, it is challenging due to the
morphological correlation between malignant and normal cells. The traditional
ALL classification strategy demands experienced pathologists to carefully read
the cell images, which is arduous, time-consuming, and often suffers
inter-observer variations. This article has automated the ALL detection task
from microscopic cell images, employing deep Convolutional Neural Networks
(CNNs). We explore the weighted ensemble of different deep CNNs to recommend a
better ALL cell classifier. The weights for the ensemble candidate models are
estimated from their corresponding metrics, such as accuracy, F1-score, AUC,
and kappa values. Various data augmentations and pre-processing are
incorporated for achieving a better generalization of the network. We utilize
the publicly available C-NMC-2019 ALL dataset to conduct all the comprehensive
experiments. Our proposed weighted ensemble model, using the kappa values of
the ensemble candidates as their weights, has outputted a weighted F1-score of
88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary
test set. The qualitative results displaying the gradient class activation maps
confirm that the introduced model has a concentrated learned region. In
contrast, the ensemble candidate models, such as Xception, VGG-16,
DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and
scatter learned areas for most example cases. Since the proposed kappa
value-based weighted ensemble yields a better result for the aimed task in this
article, it can experiment in other domains of medical diagnostic applications.
