BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

Abstract

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.

Keywords: Bayesian optimization; CNN; classification; convolutional neural network; deep learning; hyperparameter optimization; leukemia.

Figure 1

Framework of the proposed study.

Figure 2

Preprocessing procedures applied to the input ALL-IDB datasets.

Figure 3

Sample of augmented microscopic blood images; (a) original; (b) vertically reflected; (c) horizontally reflected; (d) 90$°$rotated; (e) 45$°$ rotated; (f) −45$°$ rotated.

Figure 4

Proposed CNN architecture and Bayesian Optimization for Hyperparameter tuning.

Figure 5

Architecture of the optimal Bayesian-based CNN model for ALL detection.

Figure 6

Training progress plot of the optimal proposed CNN model in the 13th optimization iteration. Upper subplot shows the percent accuracy for the training and validation subsets and lower subplot presents the corresponding loss.

Figure 7

Objective function records versus the optimization iterations plot.

Figure 8

Sample test images along with their predicted classes and class probability. ALL denotes the diseased images.