Breast Cancer Diagnosis Using Scattering Wavelet Transform and Hierarchical Multilayer Perceptron Neural Network

Breast cancer has been one of the leading causes of mortality among women in the past decade. Although this type of cancer cannot be prevented due to the unknown nature of its primary causes, early diagnosis can significantly improve a patient's chances of full recovery. Mammography is a well-established tool that aids in the early detection of this disease. Various studies have been conducted to develop breast cancer detection methods; however, these efforts have often failed to achieve sufficient accuracy due to the lack of an effective feature extraction method capable of capturing essential texture characteristics and the absence of a robust classifier. In this study, scattering wavelet transform is employed to extract texture-based features from medical images. The use of multiple features increases the dimensionality of input data for the classifier, necessitating an effective dimensionality reduction approach. To address this, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been applied. Finally, a hierarchical multilayer perceptron (MLP) neural network is utilized as the classifier for cancer detection. To evaluate the proposed method, the Mini-MIAS dataset has been used, achieving an accuracy of ۹۷.۵۷%.

کلیدواژه ها:

breast cancer ، Scattering Wavelet Transform ، Principal component analysis (PCA) ، Linear Discriminant Analysis (LDA) ، Hierarchical Classification