An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Ahmad Almadhor; Usman Sattar; Abdullah Al Hejaili; Uzma Ghulam Mohammad; Usman Tariq; Haithem Ben Chikha

doi:10.3389/fncom.2022.1083649

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Front Comput Neurosci. 2022 Nov 24:16:1083649. doi: 10.3389/fncom.2022.1083649. eCollection 2022.

Authors

Ahmad Almadhor¹, Usman Sattar², Abdullah Al Hejaili³, Uzma Ghulam Mohammad⁴, Usman Tariq⁵, Haithem Ben Chikha¹

Affiliations

¹ Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
² Department of Management Science, Beaconhouse National University, Lahore, Pakistan.
³ Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia.
⁴ Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan.
⁵ Department of Management Information Systems, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

Abstract

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

Keywords: acute lymphocytic leukemia; feature extraction; feature selection; leukemia prediction; machine learning; voting algorithm.