SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks

Wang, Jing; Lyu, Shubin; Chen, C. L. Philip; Zhao, Huimin; Chun, Lin; Quan, Pingsheng

doi:10.1007/s10845-021-01897-7

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…DeepBLS [31] combines a comprehensive learning system with deep feature extraction to identify different tissue components in colon cancer histological images. SPRBF-ABLS [32] is a cascaded neural network framework based on a sparse polynomial-based RBF neural network and an attention-based broad learning system.…”

Section: Broad Learning Systems In Medical Imagesmentioning

confidence: 99%

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

Zhang,

Qian,

et al. 2024

IEEE Access

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Retinal disease detection and diagnosis relying solely on artificial retinal diseases will bring great pressure to doctors and increase the rate of misdiagnosis. Therefore, the development of computer vision technology has brought the possibility for ophthalmologists to use computer-aided diagnosis. In recent years, most models for retinal disease recognition have been based on deep learning, which has the disadvantage of requiring large amounts of data and training time. It is also partly based on broad learning and its disadvantages are that feature extraction ability is limited and poor scalability. To overcome these limitations, we propose a novel artificial intelligence-based approach for automatic assessment of retinal disease images called multi-view deep-broad learning network (MDBL-Net), which absorbs the advantages of deep learning and broad learning. MDBL-Net comprises a Multi-view and Multi-scale Feature Extraction (MMFE) module and a Multi-scale Aggregation (MA) block. The MMFE module extracts features of different scales by learning feature representations from multiple views, while the MA block fully aggregates multi-scale deep-broad features from low-level to high-level representations. Experiments were conducted on two public datasets, UCSD and OCT2017. Experiments were conducted on two public datasets, UCSD and OCT2017, and results demonstrate that MDBL-Net achieves high accuracy even with limited training data (only 1%) and significantly reduces training time compared to traditional deep learning models. Specifically, MDBL-Net achieved an accuracy of 96.93% on the UCSD dataset and 99.90% on the OCT2017 dataset, outperforming state-of-the-art models in both cases. These findings suggest that the proposed MDBL-Net approach holds great promise for the task of retinal disease screening and recognition." INDEX TERMS Broad learning, image classification, medical image, retinal disease, computer aided diagnosis.

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Section: Broad Learning Systems In Medical Imagesmentioning

confidence: 99%