DHBLSN: A diligent hierarchical broad learning system network for cogent polyp segmentation

Abstract

In medical practice, polyp segmentation holds immense significance for early Colorectal Cancer diagnosis. Over the past decade, techniques based on Deep Learning (DL) have been used extensively for segmentation purposes, showcasing promising performance. However, the efficacy of DL methods comes with a trade-off as they require manipulating many parameters for better performance, which increases the computation cost and takes a large amount of training time. In this study, we unfold a new perspective on polyp segmentation based on a broad learning system (BLS) namely dHBLSN: A diligent Hierarchical Broad Learning System Network for Cogent Polyp Segmentation that does not require multiple layers for training which significantly reduces the computational cost. The proposed dHBLSN is characterized by multi-feature extraction and hierarchical structure. Firstly, the colonoscopy images were partitioned into non-overlapping patches, which was beneficial for extracting local spatial information. Subsequently, Multi-scale features namely “Single Iteration forward pass convolution (Sifp-conv)” feature and “2D Dual Tree-Complex Wavelet Transform (2D-DT-CWT)” feature were extracted from each patch and were used directly as the feature nodes and no explicit feature mapping was required, unlike other BLS variants. These multiscale features are then enhanced and trained separately within two parallel BLS, each with its own set of feature and enhancement nodes. A separate fusion BLS was used to stack the output of the two parallel BLS hierarchically. Additionally, we introduced diligence parameter (d), to regulate the possibility of sudden divergence of the already learned and settled network during incremental learning, ensuring network stability. We performed extensive experiments on two public datasets, namely CVC-Clinic and Kvasir-SEG. Our proposed framework achieved Dice Coefficient (DC)- 0.889, Precision-0.917, Recall-0.906, F1-0.911, and F2-0.908 on the CVC-Clinic dataset. For the Kvasir-SEG dataset, our method achieved Dice Coefficient (DC)- 0.909, Precision-0.924, Recall-0.909, F1-0.916, and F2-0.911. Cross-dataset validation further underscores the generalization capability of dHBLSN, affirming its clinical relevance. Comparative analysis against state-of-the-art models highlights dHBLSN’s superior balance between effectiveness and computational complexity.