Edge-aware dual path network for medical image classification (original) (raw)
References
Abhisheka, B., Biswas, S.K., Purkayastha, B., Das, D., Escargueil, A.: Recent trend in medical imaging modalities and their applications in disease diagnosis: a review. Multimed. Tools Appl. 83(14), 43035–43070 (2024) Article Google Scholar
Parul, S.A., Shukla, S.: Novel techniques for early diagnosis and monitoring of Alzheimer’s disease. Expert Revi. Neurotherap. 25(1), 29–42 (2025) Article Google Scholar
Wang, J., Wang, S., Zhang, Y.: Deep learning on medical image analysis. CAAI Trans. Intell. Technol. 10(1), 1–35 (2025) Article Google Scholar
Li, X., Li, M., Yan, P., Li, G., Jiang, Y., Luo, H., Yin, S.: Deep learning attention mechanism in medical image analysis: basics and beyonds. Int. J. Network Dyn. Intell. 8, 93–116 (2023) Google Scholar
Kumar, S.S.: Advancements in medical image segmentation: a review of transformer models. Comput. Electr. Eng. 123, 110099 (2025) Article Google Scholar
Salehi, Y., Giannacopoulos, D.: Physgnn: a physics-driven graph neural network based model for predicting soft tissue deformation in image-guided neurosurgery. Adv. Neural. Inf. Process. Syst. 35, 37282–37296 (2022) Google Scholar
Zangana, H.M., Mohammed, A.K., Mustafa, F.M.: Advancements in edge detection techniques for image enhancement: a comprehensive review. Int. J. Artif. Intell. Robot. 6(1), 29–39 (2024) Article Google Scholar
Dong, S., Gong, Y., Shi, J., Shang, M., Tao, X., Wei, X., Zhou, T.: Brain cognition-inspired dual-pathway cnn architecture for image classification. IEEE Trans. Neural Networks Learn. Syst. 35(7), 9900–9914 (2023) Article Google Scholar
Yang, Y., Fu, H., Aviles-Rivero, A.I., Schönlieb, C.B., Zhu, L.: Diffmic: dual-guidance diffusion network for medical image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 95–105 (2023)
Chang, Y., Zheng, Z., Sun, Y., Zhao, M., Lu, Y., Zhang, Y.: Dpafnet: a residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation. Biomed. Signal Process. Control 79, 104037 (2023) Article Google Scholar
Ji, Q., Wang, J., Ding, C., Wang, Y., Zhou, W., Liu, Z., Yang, C.: Dmagnet: dual path multi?scale attention guided network for medical image segmentation. IET Image Proc. 17(13), 3631–3644 (2023) Article Google Scholar
Musaev, J., Anorboev, A., Anorboeva, S., Seo, Y.S., Nguyen, N.T., Hwang, D.: Hybrid convolutional network fusion: enhanced medical image classification with dual-pathway learning from raw and enhanced visual features. In: International Conference on Computational Collective Intelligence, pp. 120–132 (2024)
Cao, L., Pan, K., Ren, Y., Lu, R., Zhang, J.: Multi-branch spectral channel attention network for breast cancer histopathology image classification. Electronics 13(2), 459 (2024) Article Google Scholar
Liu, S., Yue, W., Guo, Z., Wang, L.: Multi-branch cnn and grouping cascade attention for medical image classification. Sci. Rep. 14(1), 15013 (2024) Article Google Scholar
Bakkouri, I., Afdel, K.: Mlca2f: multi-level context attentional feature fusion for covid-19 lesion segmentation from ct scans. SIViP 17, 1181–1188 (2023) Article Google Scholar
Bakkouri, I., Afdel, K.: Bg-3dm2f: bidirectional gated 3d multi-scale feature fusion for Alzheimer’s disease diagnosis. Multimed. Tools Appl. 81, 10743–10776 (2022) Article Google Scholar
Bakkouri, I., Afdel, K.: Computer-aided diagnosis (cad) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed. Tools Appl. 79, 20483–20518 (2019) Article Google Scholar
Li, J., Huang, L., Zhai, Y., Ling, S., Ouyang, H., Li, L.: Mff-ibl: a lightweight cascade network based on multi-branch feature fusion and incremental broad learning for lead-independent ecg classification. IEEE Trans. Instrument. Meas. (2024)
Liu, S., Wang, W., Deng, L., Xu, H.: Cnn-trans model: a parallel dual-branch network for fundus image classification. Biomed. Signal Process. Control 96, 106621 (2024) Article Google Scholar
Jiang, X., Zhu, Y., Liu, Y., Wang, N., Yi, L.: Mc-dc: an mlp-cnn based dual-path complementary network for medical image segmentation. Comput. Methods Programs Biomed. 242, 107846 (2023) Article Google Scholar
Mandal, P.K., Mahto, R.V.: Deep multi-branch cnn architecture for early Alzheimer’s detection from brain mris. Sensors 23(19), 8192 (2023) Article Google Scholar
Khoramipour, S., Gandomkar, M., Shakiba, M.: Enhancement of brain tumor classification from mri images using multi-path convolutional neural network with svm classifier. Biomed. Signal Process. Control 93, 106117 (2024) Article Google Scholar
Sun, L., Tian, H., Ge, H., Tian, J., Lin, Y., Liang, C., Zhao, Y.: Cross-attention multi-branch cnn using dce-mri to classify breast cancer molecular subtypes. Front. Oncol. 13, 1107850 (2023) Article Google Scholar
Abdelhalim, I., Badawy, M.A., Abou El-Ghar, M., Ghazal, M., Contractor, S., Bogaert, E., El-Baz, A.: Multi-branch cnnformer: a novel framework for predicting prostate cancer response to hormonal therapy. Biomed. Eng. Online 23(1), 131 (2024) Article Google Scholar
Tang, Z.Y., Lin, Y.C., Shen, C.C.: Dual-path convolutional neural network for chronic kidney disease classification in ultrasound echography. In: 2022 IEEE International Ultrasonics Symposium (IUS), pp. 1–4. IEEE (2022)
Fang, L., Wang, X.: Ultrasound covid-19 classification based on the novel module-based dual-path network. IEEE Trans. Artif. Intell. 5(3), 1040–1051 (2022) Article Google Scholar
Zhou, Y., Yang, X., Yin, J., Liu, S.: Research on multi-scale feature fusion network algorithm based on brain tumor medical image classification. Comput. Mater. Contin. 79(3), 285 (2024) Google Scholar
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2015)
Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Howard, A.: Mobilenetv4: universal models for the mobile ecosystem. In: European Conference on Computer Vision (ECCV), pp. 78–96. Springer (2024)
Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: Proceedings of the International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, pp. 10096–10106. PMLR (2021)
Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: Repvit: revisiting mobile cnn from vit perspective. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15909–15920 (2024)
Cui, C., Gao, T., Wei, S., Du, Y., Guo, R., Dong, S., Lu, B., Zhou, Y., Lv, X., Liu, Q., Hu, X., Yu, D., Ma, Y.: PP-LCNet: a lightweight CPU convolutional neural network. Preprint at https://arxiv.org/abs/2109.15099 (2021)