Advanced Deep Learning Techniques Transform Acute Lymphoblastic Leukemia Classification

Introduction to Acute Lymphoblastic Leukemia

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year, comprising nearly 25% of pediatric cancer cases.

Challenges in Traditional Diagnosis

Many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge.

Introducing the DDRNet Model

Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. This study presents the Deep Dilated Residual Convolutional Neural Network (DDRNet) focusing on the classification of blood cell images.

Key Features of DDRNet

• Incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence.
• Employs Global and Local Feature Enhancement Blocks (GLFEB) to improve feature normalization.
• Uses Channel and Spatial Attention Block (CSAB) to emphasize specific feature channels.

Performance and Results

The model, analyzed with a Kaggle dataset of 16,249 images, achieved a classification accuracy of 99.86% with a minimal computational complexity. DDRNet’s feature discrimination ability boosted the F1 score to 0.96, showcasing its superior performance in the field.

Conclusion

The DDRNet model stands out due to its high testing accuracy of 91.98%. The strategic combination of the various blocks contributes to the improved feature discrimination crucial for accurate multi-class blood cell image identification.