Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer
  Learning Method
Wahidul Hasan Abir Md. Fahim Uddin Faria Rahman Khanam Mohammad Monirujjaman Khan
This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of
blood cancer prevalent in children and teenagers, characterized by the rapid
proliferation of immature white blood cells (WBCs). These atypical cells can
overwhelm healthy cells, leading to severe health consequences. Early and
accurate detection of ALL is vital for effective treatment and improving
survival rates. Traditional diagnostic methods are time-consuming, costly, and
prone to errors. The paper proposes an automated detection approach using
computer-aided diagnostic (CAD) models, leveraging deep learning techniques to
enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes
various transfer learning models like ResNet101V2, VGG19, InceptionV3, and
InceptionResNetV2 for classifying ALL. The methodology includes using the Local
Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and
reliability of the AI system's predictions. This approach is critical for
overcoming the "black box" nature of AI, where decisions made by models are
often opaque and unaccountable. The paper highlights that the proposed method
using the InceptionV3 model achieved an impressive 98.38% accuracy,
outperforming other tested models. The results, verified by the LIME algorithm,
showcase the potential of this method in accurately identifying ALL, providing
a valuable tool for medical practitioners. The research underscores the impact
of explainable artificial intelligence (XAI) in medical diagnostics, paving the
way for more transparent and trustworthy AI applications in healthcare.