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Blood cancer prediction model based on deep learning technique.

Amr I Shehta1, Mona Nasr2, Alaa El Din M El Ghazali3

  • 1Department of Information System, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt. amr.ibrahim@fci.helwan.edu.eg.

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|January 13, 2025
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Summary

This study enhances blood cancer diagnosis using deep learning. ResNetRS50 demonstrated superior accuracy and speed for early detection, improving patient survival rates.

Keywords:
Blood cancerClassificationConvnextMedical deep learningRegNetX016ResNetRS50VGG19

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Oncology
  • Computational Biology

Background:

  • Blood cancer poses a significant global health threat, with high mortality rates linked to genetic and environmental factors.
  • Early cancer detection is crucial for improving treatment success rates and reducing mortality.
  • Current diagnostic methods require enhancement to meet the urgent need for timely and accurate blood cancer identification.

Purpose of the Study:

  • To improve the accuracy and efficiency of blood cancer diagnosis through advanced deep learning models.
  • To identify the most effective deep learning architecture for early blood cancer detection.
  • To reduce blood cancer-related mortality by facilitating earlier and more precise diagnoses.

Main Methods:

  • Evaluation of multiple deep learning models including ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19.
  • Comparative analysis of model performance based on accuracy, speed, and error rates for blood cancer classification.
  • Focus on leveraging deep learning for enhanced image or data analysis in blood cancer diagnostics.

Main Results:

  • ResNetRS50 exhibited superior performance compared to other state-of-the-art models in terms of accuracy and speed.
  • The ResNetRS50 model achieved minimal error rates, indicating high diagnostic precision.
  • Deep learning approaches show significant potential for advancing blood cancer detection capabilities.

Conclusions:

  • ResNetRS50 is a highly effective deep learning model for improving blood cancer diagnosis.
  • Early detection facilitated by advanced AI can significantly reduce mortality and improve patient outcomes.
  • This research supports the integration of deep learning into clinical practice for better blood cancer management.