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An Efficient Computer Vision-based Approach for Acute Lymphoblastic Leukemia Prediction by Ahmad Almadhor; Usman Sattar; Abdullah Al Hejaili; Uzma Ghulam Mohammad; Usman Tariq; Haithem Ben Chikha is a Neuroscience article available to read on EtoBox.

What is An Efficient Computer Vision-based Approach for Acute Lymphoblastic Leukemia Prediction about?

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 tech

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Author

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

Publisher

Frontiers Media SA

Published

2022

Language

EN

Field

Neuroscience (Life Sciences)