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SqueezeCapsNet: enhancing capsule networks with squeezenet for holistic medical and complex images

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Abstract

Early diagnosis of patients’ disease is crucial since it helps doctors and patients devise a treatment plan. Therefore, recognizing medical images using Artificial intelligence-based deep learning techniques has recently increased. Capsule Network (CapsNet) has promising methods in visual tasks due to its ability to keep a high relationship of spatial information compared to convolutional neural networks (CNNs). However, CapsNet faces a critical problem with a complex image background that limits its performance. The traditional CapsNet adopts a standalone convolution (SC) as a feature extractor, Softmax function for normalization of coupling coefficient, and dynamic routing procedure to allow active capsules to perform predictions leading to activation of high-level capsules. The SC is not an effective feature extractor, and SoftMax impedes capsules from distributing optimal coupling coefficient during routing. This paper proposes a CapsNet architecture called SqueezeCapsNet that integrates SqueezeNet and CapsNet to achieve effective feature extraction and fewer parameters. A new squash function named parametric squash function (PSF) was proposed to reduce non-informative capsules and promote discriminative capsules. To the best of our knowledge in literature, we are the first to integrate SqueezeNet into CapsNet. We evaluate our framework on two medical image datasets; Brain tumor and Lung & Colon cancer datasets. Additionally, datasets with varied backgrounds; MNIST, fashion-MNIST, CIFAR-10 were used to evaluate the robustness and generalizability of the model. The SqueezeCapsNet produces 94.85%, 99.76%, 99.87%, 93.49%, and 82.45% on Brain tumor, Lung & Colon Cancer, MNIST, fashion-MNIST, and CIFAR-10 datasets, respectively. Experimental results show that the proposed architecture’s compression techniques significantly provide fewer parameters while enhancing stability and accuracy across all the evaluation metrics. Our results show that our method improves CapsNet and can be adopted as a computer-aided diagnostic method to support the diagnosis of medical image tasks.

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Data Availability

The MNIST dataset that supports this study is publicly available at http://yann.lecun.com/exdb/mnist/[11]. The MNIST dataset that supports this study is publicly available at https://github.com/zalandoresearch/fashion-mnist [42]. The MNIST dataset that supports this study is publicly available at https://www.cs.toronto.edu/~kriz/cifar.html [20]. Finally, the lung and colon cancer histopathological images (LC25000) data that support the findings of this study are openly available at https://github.com/tampapath/lung_colon_image_set/blob/master/README.md[5].

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Acknowledgements

The authors would like to thank the editor and the reviewers for their helpful suggestions and valuable comments.

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Authors and Affiliations

  1. Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, 00233, Ghana

    Kwabena Adu, Patrick Kwabena Mensah, Mighty Abra Ayidzoe, Michael Opoku & Samuel Boateng

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China

    Joojo Walker

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Adu, K., Walker, J., Mensah, P.K. et al. SqueezeCapsNet: enhancing capsule networks with squeezenet for holistic medical and complex images. Multimed Tools Appl 83, 2823–2852 (2024). https://doi.org/10.1007/s11042-023-15089-3

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