Department
Computer Science
Degree Name
Master of Science (MS)
Abstract
Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology remains limited. Cytopathology could benefit from deep learning applications by improving diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box” that keeps the models from being transparent and trustworthy for widespread clinical adoption. We conducted a comprehensive and comparative analysis of several deep learning architectures for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from peripheral blood smear images. The models in this research include a Vision Transformer (ViT) and a diverse selection of Convolutional Neural Network (CNN) models. The performance of the models was assessed using F1-score, AUC, precision, and recall. The top model achieving an F1-score of 99.94%. In order to address the “black box” issue a multi-faceted explainable AI (XAI) analysis was performed by utilizing methods such as Grad-CAM, Score-CAM, LIME, and Integrated Gradients. These techniques provide visual explanations that present a qualitative comparison of the architectures’ feature attribution. This showcases the cellular morphologies that were weighted the most for any given prediction. This research seeks to provide an evaluation of model performance as well as a crucial analysis of interpretability layers that can contribute to increased trustworthiness and transparency for the use of deep learning in diagnostics.
Keywords
ML, XAI, leukemia, cytopathology, computer vision
Advisor
Dr. Anas Hourani
Date of Award
Fall 2025
Document Type
Thesis
Recommended Citation
Garrett, Chase A., "Deconstructing the Black Box: An Explainability Analysis of Deep Learning Architectures in Cytopathology" (2025). Master's Theses. 3272.
Available at:
https://scholars.fhsu.edu/theses/3272
Rights
© The Author
