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Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model.

Javaria Amin1, Muhammad Sharif2, Steven Lawrence Fernandes3

  • 1Department of Computer Science, University of Wah, Quaid Avenue, Wah Cantt, Pakistan, 4740, Pakistan.

Microscopy Research and Technique
|January 19, 2022
PubMed
Summary

This study introduces a hybrid deep learning and quantum computing model for accurate breast cancer detection. The model achieves high accuracy in classifying tumors as benign or malignant, aiding pathologists and improving diagnostic speed.

Keywords:
4-qubit-quantum circuitReLUbreast cancerdeeplabv3health carepublic healthxception

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Area of Science:

  • Oncology
  • Computer Science
  • Quantum Computing

Background:

  • Visual inspection of histopathological samples is the gold standard for breast cancer diagnosis but is time-consuming and subjective.
  • Deep learning models offer potential for improving diagnostic accuracy and efficiency in histopathology.
  • Challenges remain in accurately detecting and classifying breast tumors due to image complexity and tissue variations.

Purpose of the Study:

  • To develop and evaluate an improved semantic segmentation model for breast cancer detection.
  • To integrate deep learning with quantum computing for enhanced breast malignancy classification.
  • To reduce pathologist workload and diagnostic subjectivity in breast cancer diagnosis.

Main Methods:

  • Utilized pre-trained Xception and DeepLabV3+ models for semantic segmentation of breast ultrasound images.
  • Trained the segmentation model on images with ground masks to classify tissues as benign or malignant.
  • Integrated segmented images and histopathological data into a 4-qubit quantum circuit with a six-layered architecture.

Main Results:

  • The semantic segmentation model achieved over 99% accuracy in classifying breast ultrasound images.
  • The hybrid deep learning and quantum framework demonstrated high performance in detecting breast malignancy.
  • The proposed model achieved 95% accuracy for breast microscopic cancer classification and 99% for malignancy detection.

Conclusions:

  • The hybrid semantic model effectively classifies breast cancer as benign or malignant with high accuracy.
  • The integration of deep learning and quantum computing presents a promising approach for breast cancer diagnosis.
  • This framework has the potential to significantly improve the speed and accuracy of breast cancer detection.