Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification

Acute lymphoblastic leukaemia (ALL), a common form of cancer, remains a life-threatening condition that affects individuals worldwide, including both adults and children. The prognosis is significantly worse when the disease is identified at an advanced stage, as the chances of successful treatment diminish. Diagnosis typically requires invasive and costly procedures, which can delay timely intervention. Images from peripheral blood smears (PBS) have been useful for the preliminary screening of ALL in probable cases. However, due to the nonspecific presentation of the disease, interpreting these images poses significant challenges, increasing the risk of misdiagnosis. We have proposed an approach that uses 20,000 PBS images to precisely enable the early diagnosis of ALL and its distinct subtypes (benign, malignant-early Pro-B, malignant-Pro-B, and malignant-Pre-B) in order to overcome these problems. Our proposed system uses colour normalized PBS images using Vahadane method along with the feature extraction capabilities of deep neural networks combined with a stacking ensemble learning approach that integrates various machine learning algorithms for classification. This system is capable of distinguishing ALL cases from haematogones and accurately identifying its subtypes. By streamlining the diagnostic process, the platform aims to reduce the effort and time required by clinicians and patients. Our proposed model highlight the system's effectiveness, with a comparative analysis of established machine learning algorithms showcasing its superior performance. The proposed model achieved exceptional accuracy (99.95%), recall (99.95%), precision (99.95%) and F1-score (99.95%) for the early detection of ALL and its variants, indicating its potential as a proof-of-concept system, while requiring further validation before clinical deployment.