Background:
Breast cancer is considered as the most perilous sickness among females
worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient
algorithms to diagnose breast cancer at early stages, which have increased the efficiency and
performance by utilizing the learned features of gold standard histopathological images.
Objective:
Most of these systems have either used traditional handcrafted features or deep features
which had a lot of noise and redundancy, which ultimately decrease the performance of the system.
Methods:
A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and
deep features to classify the breast cancer images. HOG and LBP features are serially fused with
pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification
performance of proposed method.
Results:
The method concentrates on histopathological images to classify the breast cancer. The
performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of
97.2% and image-level accuracy of 96.7% is recorded.
Conclusion:
The proposed hybrid method achieves the best performance as compared to previous
methods and it can be used for the intelligent healthcare systems and early breast cancer detection.
An intelligent healthcare system for predicting and preventing dengue virus infection
