Transactions on Machine Intelligence 2821-1693 2 4 2019 12 01 Breast Cancer Diagnosis Using Scattering Wavelet Transform and Hierarchical Multilayer Perceptron Neural Network 213 220 220767 10.47176/TMI.2019.213 EN M. Nouri Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran N. Nematbakhsh Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran S. Farrokhi Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran Journal Article 2025 05 13 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 97.57%. https://www.tmachineintelligence.ir/article_220767_5b82807fea535595433cc4dd03e76b4c.pdf