An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer

Abstract

This work proposes an effective sparse-based hybrid alternating deep-layer cascade model (HADLCM) for breast cancer detection. It is achieved by cascading sparse and collaborative representation alternatively, with a softmax vector representation at the interface of each cascade segment. Here, alternating deep-layer cascade model (ADLCM) followed by Laplacian of Gaussian-based modified high boosting filter (LoGMHBF) integrated with projection to discriminative subclasses (PDS)-based pre-processing is hybridized with ADLCM subsequent to PDS to improve feature discrimination ability. A class discriminant softmax vector representation is employed to cascade sparse and collaborative representations, leveraging the benefits of both. It improves hierarchical learning abilities by expanding shallow-sparse representation to an effective multi-layer learning approach for extracting deep-rooted discriminative information. The experimental results show the superiority of HADLCM to identify abnormality and malignancy with the accuracy of 98.17% and 97.75% in mini-DDSM (Digital Database for Screening Mammography); 94.87% and 95.00% in BUSI (Breast Ultrasound Image)BUSI, respectively.