Acute Lymphoblastic Leukemia (ALL), a cancer affecting the blood and bone marrow,

requires precise classification for accurate diagnosis, personalized treatment plans, and improved predictive

assessments to enhance patient survival and quality of life. This study presents LEU3, a novel classification

model designed to improve the accuracy of leukemia detection from peripheral blood smear (PBS) images.

LEU3 leverages an attention-based convolutional neural network (CNN) architecture, incorporating pooling

layers, a global average pooling layer, and dense layers with dropout for regularization. The model is

trained with an Adam optimizer comprising with four classes: Benign, early malignant pre-B, malignant

pre-B, and malignant pro-B. Data augmentation techniques were employed to increase training set diversity.

Additionally, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations

(LIME) are used to enhance interpretability and transparency in the model’s decision-making process. LEU3

achieved a test accuracy of 99% and a validation accuracy of 99% on 484 PBS images, demonstrating a 3%

improvement over the baseline model. These results underline the potential of LEU3 in supporting medical

professionals by reducing diagnostic workload and improving the accuracy of leukemia classification