Title:Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional
Neural Networks (CNNs)
Volume: 19
Author(s): Annal A. Albeeshi*Hanan S. Alshanbari
Affiliation:
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
Keywords:
Cancer statistics, leukemia, ALL leukaemia, CNNs, VGG16, transfer learning, classification SVM & MLP, feature extraction.
Abstract:
Background: The techniques differed in many of the literature on the detection of Acute
Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the
Kingdom of Saudi Arabia were increasing and the causes of this disease were not known, especially
for children, which is a serious and fatal disease.
Objective: Through this work we seek to contribute to discover the blood cells affected by Acute Lymphocytic
Leukem and to find an effective and fast method and to have the correct diagnosis as the time
factor is important in the diagnosis and the initiation of treatment. which is based on one of the deep
learning techniques that specialize in very deep networks, the use of one of the CNNs is VGG16.
Methods: Detection scheme is implemented by pre-processing, feature extraction, model building, fine
tuning method, classification are executed. By using VGG16 pre-trained, and using SVM and MLP
classification algorithms in Machine Learning.
Results: Our results are evaluated based on criteria, such as Accuracy, Precision, Recall, and F1-Score.
The accuracy results for SVM classifier MLP of 77% accuracy at 0.001 learning rate and the accuracy
for SVM classifier 75% at 0.005 learning rate. Whereas, the best accuracy result for VGG16 model
was 92.27% at 0.003 learning rate. The best validation accuracy result was 85.62% at 0.001 learning
rate.
