Towards Explainable Deep Learning in Oncology: Integrating EfficientNet-B7 with XAI techniques for Acute Lymphoblastic Leukaemia

Acute Lymphoblastic Leukaemia (ALL), presents a potential risk to human health due to its rapid progression and impact on the body’s blood-producing system. The accurate diagnosis derived through investigations plays a crucial role in formulating effective treatment plans that can influence the likelihood of patient recovery. In the pursuit of improving diagnostic accuracy, diverse Machine Learning (ML) and Deep Learning (DL) approaches have been employed, demonstrating significant improvement in analyzing intricate biomedical data for identifying ALL. However, the complex nature of these algorithms often makes them difficult to comprehend, posing challenges for patients, medical professionals, and the wider community. To address this issue, it is essential to clarify the functioning of these ML/DL models, strengthen trust and providing users with a clearer understanding of diagnostic outcomes. This paper introduces an innovative framework for ALL diagnosis by incorporating the EfficientNet-B7 architecture with Explainable Artificial Intelligence (XAI) methods. Firstly, the proposed model accurately classified the ALL utilizing C-NMC-19 and Taleqani Hospital datasets. The efficacy of the proposed model was rigorously validated utilizing established evaluation metrics notably AUC, mAP, Accuracy, Precision, Recall, and F1-score. Secondly, the XAI approaches namely, Grad-CAM, LIME and IG were applied to explain the proposed model decision. Our contributions on pioneering the explanation of EfficientNet-B7 decisions using XAI for the diagnosis of ALL, set a new benchmark for trust and transparency in the medical field.