Deep Learning Models for Accurate Leukemia & Lymphoma Detection

Acute lymphoblastic leukemia (ALL) and Lymphoma are important diseases that need to be detected early, but detection with traditional methods may be slow and inconsistent. Although current AI methods present potential advantages, they are frequently limited by their reliance on small input data, overfitting, and a lack of external verification, which are unfavorable for their clinical implementation. This paper proposes a unified framework for creating a shared deep learning model for dual disease detection, based on the ConvNeXt design. ConvNeXt's superiority was demonstrated in a first ablation, wherein the Model was compared to Swin Transformer, ResNet101, VGG16, and a custom CNN on an ALL dataset, achieving a best accuracy of 99.69%. The selected ConvNeXt model was then optimized and retrained on a larger dataset consisting of both ALL and Lymphoma samples. As a result, there was only one highly consistent diagnostic tool that attained an accuracy of 99.72% on the mixed test set. More importantly, the practical utility of the framework in obtaining significant results was validated through extensive testing using completely new, previously unseen external datasets. The framework demonstrated an outstanding degree of generalizability, achieving 100% accuracy on an independent ALL dataset and 97.73% on an independent Lymphoma dataset. This work presents a well-represented, fully automated, and externally validated hematological diagnosis system that demonstrates a possible route of implementing trusted AI in direct care practice.