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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Sliding window based deep ensemble system for breast cancer classification.

Amin Alqudah1, Ali Mohammad Alqudah2

  • 1Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.

Journal of Medical Engineering & Technology
|March 26, 2021
PubMed
Summary

This study introduces a novel deep learning method using an ensemble of four convolutional neural networks (CNNs) for accurate breast cancer classification from histopathological images. The approach achieved 99.33% accuracy, improving early detection and reducing mortality rates.

Keywords:
Breast cancerdeep learningensemblehistopathological imagestransfer learning

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Area of Science:

  • Oncology
  • Computer Science
  • Medical Imaging

Background:

  • Breast cancer poses a significant global health challenge, with early detection crucial for reducing mortality rates.
  • Accurate classification of breast cancer from histopathological images remains a complex task.
  • Deep learning methods show promise for high-performance breast cancer screening.

Purpose of the Study:

  • To develop and evaluate a novel deep learning approach for classifying breast cancer into eight distinct types using histopathological images.
  • To enhance the accuracy of breast cancer detection through an ensemble method combining multiple pre-trained convolutional neural networks (CNNs).

Main Methods:

  • A sliding window technique was applied to histopathological images from the BreakHis database.
  • Four pre-trained CNNs (GoogleNet, AlexNet, ResNet50, DenseNet-201) were utilized in an ensemble approach.
  • The method was tested on images with magnifications of 40x, 100x, 200x, and 400x.

Main Results:

  • The proposed ensemble technique achieved a high accuracy of 99.3325% in classifying breast cancer histopathological images.
  • The system demonstrated effective classification across all tested magnification levels.
  • The results are comparable to those of recent studies in the field.

Conclusions:

  • The developed ensemble deep learning method offers a highly accurate and effective solution for breast cancer classification.
  • This approach has the potential to significantly improve the accuracy and efficiency of breast cancer screening and diagnosis.
  • Further research can explore the application of this method to larger datasets and other cancer types.