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Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.

Wahidul Hasan Abir1, Md Fahim Uddin1, Faria Rahman Khanam1

  • 1Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka-1229, Bangladesh.

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This summary is machine-generated.

This study introduces an automated method for detecting acute lymphoblastic leukemia (ALL) using deep learning and explainable AI. The system achieved 98.38% accuracy, offering a reliable tool for early cancer diagnosis.

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Leukemia, particularly Acute Lymphoblastic Leukemia (ALL), is a dangerous cancer stemming from abnormal white blood cell (WBC) production.
  • Current ALL diagnosis methods are time-consuming, costly, and prone to errors, necessitating more accurate and efficient detection techniques.
  • Deep learning (DL) offers potential for automated medical diagnosis, but the 'black box' nature of AI models hinders trust and reliability.

Purpose of the Study:

  • To develop and evaluate an automated system for the accurate detection and classification of Acute Lymphoblastic Leukemia (ALL).
  • To enhance the reliability and trustworthiness of AI-driven diagnostic tools through explainable artificial intelligence (XAI).
  • To reduce the workload of medical professionals and improve diagnostic accuracy in leukemia detection.

Main Methods:

  • Utilized transfer learning models (InceptionV3, ResNet101V2, VGG19, InceptionResNetV2) for automated ALL classification from medical images.
  • Implemented the Local Interpretable Model-Agnostic Explanations (LIME) technique to provide transparency and interpretability for the AI model's predictions.
  • Compared the performance of various transfer learning models for ALL detection accuracy.

Main Results:

  • The proposed method, utilizing the InceptionV3 model, achieved a high accuracy of 98.38% in classifying ALL.
  • Comparative analysis showed the InceptionV3 model outperformed other transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2.
  • The integration of LIME confirmed the validity and reliability of the classification results, explaining the basis for specific diagnoses.

Conclusions:

  • The developed automated system demonstrates high accuracy and reliability for identifying ALL, offering a significant advancement over manual diagnostic methods.
  • Explainable AI (XAI) through LIME successfully addresses the trust and liability concerns associated with DL models in medical applications.
  • This AI-powered approach holds promise for assisting medical examiners in the early and precise detection of leukemia, potentially improving patient outcomes.