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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.

Vatsala Anand1, Prabhnoor Bachhal1, Deepika Koundal2,3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

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Summary

A deep optimized Convolutional Neural Network (CNN) aids in the early diagnosis of Acute Lymphoblastic Leukemia (ALL). This AI model achieved high accuracy and precision, offering a promising tool for detecting this bone marrow cancer.

Keywords:
Acute lymphoblastic leukemiaConvolutional neural network.DiseaseHematological

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Area of Science:

  • Hematology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Acute Lymphoblastic Leukemia (ALL) is a significant bone marrow cancer affecting children and adults.
  • Early and accurate diagnosis is crucial for effective treatment strategies and improved patient outcomes.
  • Current diagnostic methods require integration of diverse clinical, morphological, cytogenetic, and molecular data.

Purpose of the Study:

  • To develop and evaluate a deep optimized Convolutional Neural Network (CNN) for the early diagnosis and detection of ALL.
  • To assess the performance of the proposed CNN model in terms of accuracy and precision.

Main Methods:

  • A deep optimized CNN model was designed with five convolutional blocks and five max pool layers.
  • The model was trained and tuned using hyperparameters including 30 epochs and a batch size of 32.
  • The Adam and Adamax optimizers were compared for model optimization.

Main Results:

  • The deep optimized CNN model demonstrated high performance in ALL detection.
  • Using the Adam optimizer, the model achieved an accuracy of 0.96 and a precision of 0.95.
  • The proposed CNN model effectively identified key aspects for ALL diagnosis.

Conclusions:

  • Deep optimized CNNs offer a powerful approach for the early and accurate diagnosis of Acute Lymphoblastic Leukemia.
  • The developed AI model shows significant potential to aid clinicians in risk assessment and treatment planning.
  • Further research and clinical trials are essential to address challenges like resistance, relapse, and long-term toxicity in ALL management.