Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs)

Area of Science:

• Medical Imaging
• Computational Biology
• Artificial Intelligence in Healthcare

Background:

• Acute Lymphocytic Leukemia (ALL) is a serious, potentially fatal childhood cancer with increasing global incidence.
• Current diagnostic techniques for ALL from blood smears vary, and rapid, accurate methods are needed, especially given unknown causes.
• Early diagnosis and treatment initiation are critical for improving patient outcomes in ALL.

Purpose of the Study:

• To develop an effective and fast method for detecting Acute Lymphocytic Leukemia (ALL) using deep learning.
• To identify blood cells affected by ALL and enable correct diagnosis.
• To leverage deep learning, specifically Convolutional Neural Networks (CNNs) like VGG16, for ALL detection.

Main Methods:

• A detection scheme involving pre-processing, feature extraction, model building, fine-tuning, and classification was implemented.
• A pre-trained VGG16 model was utilized, combined with Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classification algorithms.
• The methodology focused on deep learning techniques specialized in very deep networks for image analysis.

Main Results:

• The VGG16 model achieved a highest accuracy of 92.27% at a 0.003 learning rate.
• SVM and MLP classifiers showed accuracies of 75% and 77% respectively.
• The best validation accuracy reached 85.62% at a 0.001 learning rate, demonstrating robust performance.

Conclusions:

• The VGG16 deep learning model offers a highly accurate and efficient approach for the detection of Acute Lymphocytic Leukemia from blood smear images.
• This AI-driven method can significantly aid in the timely diagnosis of ALL, crucial for initiating prompt treatment.
• The study highlights the potential of advanced deep learning techniques in improving diagnostic capabilities for critical diseases like ALL.