Multiscale contextual attention network for robust diagnosis of acute lymphoblastic leukemia in blood smears: Implications for clinical adoption and the role of medical science liaisons.

Objectives: Acute lymphoblastic leukemia (ALL), certain types of which are present in children, is a common childhood cancer and must be diagnosed correctly. The impact of early diagnosis on patient survival is high due to timely and appropriate treatment. However, current ALL diagnostic procedures remain laborious and prone to inaccuracy. This study aims to enhance automated ALL diagnosis by introducing a Hybrid Multi-Scale Contextual Attention Module (HMSCAM), for improved detection and subtype classification from peripheral blood smear (PBS) images. The objectives of thisstudy can be summed up in terms of capturing varying morphological features, modelling long term dependencies and dynamic recalibration of input-specific adaptation for effective classification. Methods: A pretrained ResNet50 backbone augmented with HMSCAM module is used to classify ALL into benign, Early Pre-B, Pre-B, and Pro-B categories. This enables real-time automated diagnostic feedback, with experiments conducted on 3256 PBS images from 89 patients to validate the methodology. Results: The evaluation of the model on the 3256 PBS images against binary classification task yielded an accuracy of 96.5%. Whereas multiclassification task accuracy is 93.5%, with AUC values for the binary and multiclassification tasks ranging from 0.96 to 0.97. The HMSCAM framework from this study outperformed the benchmark models with average inference time being 0.040 s per image which supports real-time clinical applicability. Conclusion: HMSCAM serves as advanced, adaptable and effective computer-aided diagnosis system. This system reduces baseline diagnostic errors while supporting personalized treatment planning for ALL. The integration of interpretable mechanisms further facilitates clinical adoption of artificial intelligence-enabled diagnostic technologies.