Sushma, B.Pulikala, A.2026-02-042024Biomedical Signal Processing and Control, 2024, 91, , pp. -17468094https://doi.org/10.1016/j.bspc.2024.105969https://idr.nitk.ac.in/handle/123456789/21157Breast cancer causes a serious menace to women's health and lives, underscoring the urgency of accurate tumor detection. Detecting both cancerous and non-cancerous breast tumors has become increasingly crucial, with ultrasound imaging emerging as a widely adopted modality for this purpose. However, identifying breast lesions in ultrasound images is a challenging task due to various tumor morphologies, geometry, similar color intensity distributions, and fuzzy boundaries, particularly irregularly shaped malignant tumors. This work proposes an encoder–decoder based U-shaped convolutional neural network (CNN) variant with an attention aggregation-based pyramid feature clustering module (AAPFC) to detect breast lesion regions. The network consists of the U-Net variant as a base network and AAPFC to fuse features extracted at the various levels of the base U-Net using a suitable feature fusion technique. Furthermore, the deformable convolution with adaptive self-attention mechanism is introduced to decode the pyramid features parallel to capture the various geometric features at multi-stages. Two public breast lesion ultrasound datasets consisting 263 malignant, 547 benign and 133 normal images are considered to evaluate the performance of the proposed model and state-of-the-art deep CNN-based segmentation models. The proposed model provides 96% accuracy, 68% Mean-IoU, 97% specificity, 82% sensitivity and 0.747 kappa score respectively. The conducted qualitative and quantitative performance analysis experiments show that the proposed model performs better in breast lesion segmentation on ultrasound images. © 2024 Elsevier LtdConvolutionConvolutional neural networksDecodingDeep learningMedical imagingSemantic WebSemanticsSignal encodingTumorsUltrasonic imagingAttention mechanismsBreast lesionBreast tumourConvolutional neural networkFeature clusteringPyramid featureSelf attention mechanismSemantic segmentationUltrasound imagesSemantic SegmentationArticlebreast lesionconvolutional neural networkdata accuracyechomammographyhierarchical clusteringhumanimage segmentationmajor clinical studyreceiver operating characteristicreceptive fieldsensitivity and specificity