Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization

Numerous studies have explored different techniques for segmenting breast cancer images, in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted attention. However, due to their down-and-out pursuance, the existing approaches like FCN (Fully Convolutional Network), PSPNe...

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Bibliographic Details
Published in:International journal of computers & applications 2023-05, Vol.45 (5), p.353-366
Main Authors: Madhukar, B. N., Bharathi, S. H., Polnaya, Ashwin M.
Format: Article
Language:English
Subjects:
Algorithms
attention mechanism Intersection of Union
Breast cancer
CAI
Computer assisted instruction
Computer networks
Convolution
Effectiveness
Feature maps
Image enhancement
Image segmentation
Machine learning
Medical imaging
multi-scale convolution
Optimization
Semantic segmentation
Semantics
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Summary:Numerous studies have explored different techniques for segmenting breast cancer images, in particular deep learning-based Computer-Aided Diagnosis (CAD) has recently netted attention. However, due to their down-and-out pursuance, the existing approaches like FCN (Fully Convolutional Network), PSPNet (Pyramid Scene Parsing Network), U-Net, and SegNet still required improvement for offering better semantic segmentation while identifying breast cancer. In this paper, the newly proposed breast cancer tumor segmentation method consists of four steps pre-processing, augmentation, segmenting image using multi-scale convolution and multi- attention mechanisms respectively. The proposed method utilizes the ResNet (Residual Network) backbone network with multi-scale convolution for feature map prediction. Also, the effectiveness of the multi-channel attention module with a pyramid dilated nodule is employed for semantic segmentation. Gated axial, position, and channel attention are combined to create a multi-channel attention mechanism. Additionally, War Search Optimization (WSO) algorithm is being utilized to enhance the accuracy of the segmented images. Experimentations are conducted on two datasets, viz., Breast Cancer Cell Segmentation Database and Breast Cancer Semantic Segmentation (BCSS) Database, with different existing networks. The effectiveness of the network is evaluated based on various criteria in terms of precision, accuracy, recall, (mean Intersection of Union), (Intersection of Union), etc.
ISSN:1206-212X
1925-7074
DOI:10.1080/1206212X.2023.2212945