An efficient computer vision-based approach for acute lymphoblastic leukemia prediction - Almadhor, Ahmad; Sattar, Usman; Al Hejaili, Abdullah; Ghulam Mohammad, Uzma; Tariq, Usman; Ben Chikha, Haithem (Frontiers in Computational Neuroscience, 2022)
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, this is acute lymphocytic leukemia (ALL), which impacts people of all ages. Thus, Timely prediction of this disease can increase the chance of survival rate, 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. We perform data preprocessing steps such as first images are cropped, then feature extraction is performed to extract the feature using pre-trained Convolutional Neural network-based deep neural network architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. ANOVA, Recursive Feature Elimination (REF), and RF as feature Selection techniques. Classification machine learning algorithms are applied to selected features. SVM with 90.0% accuracy outperforms compared to other algorithms.