i-manager Publications

Optimizing Capsule Endoscopy Detection: A Deep Learning Approach with L-Softmax and Laplacian-SGD

Sana Danish*, Nimra Shoket Ali**, Jamshaid Ul Rahman***

*-** Abdus Salam School of Mathematical Sciences, Government College University, Lahore, Pakistan.

*** School of Mathematical Sciences, Jiangsu University, Zhenjiang, China.

Periodicity:July - December'2024

Abstract

Capsule endoscopy has emerged as a non-invasive diagnostic tool for gastrointestinal diseases; however, efficient disease classification remains a challenge due to the inherent complexities of image analysis. Furthermore, the extensive time required for manual examination of capsule endoscopy images has led researchers and clinicians to seek timeefficient automated detection methods. This is where the profound advantages of deep learning (DL) become crucial. This research proposes a novel approach that combines L-Softmax with Laplacian Smoothing Stochastic Gradient Descent (LSSGD) within a ResNet architecture to enhance disease classification accuracy in capsule endoscopy images from the Kvasir dataset. The L-Softmax function is integrated into the DL framework, facilitating better class separation and feature representation. Additionally, LSSGD is employed to mitigate overfitting and enhance model generalization. Experimental results demonstrate that our methodology is stable and easy to utilize in capsule endoscopy.

Keywords

Capsule Endoscopy, Deep Learning, Laplacian Smoothing Stochastic Gradient Descent, L-Softmax.

How to Cite this Article?

Danish, S., Ali, N. S., and Rahman, J. Ul. (2024). Optimizing Capsule Endoscopy Detection: A Deep Learning Approach with L-Softmax and Laplacian-SGD. i-manager’s Journal on Mathematics, 13(2), 10-21.

References

[1]. Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375.

[2]. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). Ieee.

[3]. Allen, D. M. (1971). Mean square error of prediction as a criterion for selecting variables. Technometrics, 13(3), 469-475.

[4]. Amari, S. I. (1993). Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4-5), 185-196.

[5]. Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832.

[6]. Canann, S. A., Tristano, J. R., & Staten, M. L. (1998). An approach to combined laplacian and optimization-based smoothing for Triangular, Quadrilateral, and Quad-Dominant meshes. Indian Medical Register (IMR), 1, 479-494.

[7]. Christoffersen, P., & Jacobs, K. (2004). The importance of the loss function in option valuation. Journal of Financial Economics, 72(2), 291-318.

[8]. Gardner, W. A. (1984). Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique. Signal Processing, 6(2), 113-133.

[9]. Iakovidis, D. K., & Koulaouzidis, A. (2015). Software for enhanced video capsule endoscopy: Challenges for essential progress. Nature Reviews Gastroenterology & Hepatology, 12(3), 172-186.

[10]. Iddan, G., Meron, G., Glukhovsky, A., & Swain, P. (2000). Wireless capsule endoscopy. Nature, 405(6785), 417-417.

[11]. Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., De Lange, T., Johansen, D., & Johansen, H. D. (2020). Kvasir-seg: A segmented polyp dataset. In MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, Proceedings, Part II 26 (pp. 451-462). Springer International Publishing.

[12]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255- 260.

[13]. Ketkar, N., & Ketkar, N. (2017). Stochastic gradient descent. Deep Learning with Python: A Hands-On Introduction (pp. 113-132).

[14]. Kreusser, L. M., Osher, S. J., & Wang, B. (2023). A deterministic gradient-based approach to avoid saddle points. European Journal of Applied Mathematics, 34(4), 738-757.

[15]. Le, Q. V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., & Ng, A. Y. (2011, June). On optimization methods for deep learning. In Proceedings of the 28th International Conference on International Conference on Machine Learning (pp. 265- 272).

[16]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

[17]. Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.

[18]. Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., & Dou, D. (2022). Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12), 3197-3234.

[19]. Lin, H., & Jegelka, S. (2018). Resnet with one-neuron hidden layers is a universal approximator. Advances in Neural Information Processing Systems, 31.

[20]. Liu, Y., He, L., & Liu, J. (2019). Large margin softmax loss for speaker verification. arXiv preprint arXiv:1904.03479.

[21]. Montanelli, H., Yang, H., & Du, Q. (2019). Deep ReLU networks overcome the curse of dimensionality for bandlimited functions. arXiv preprint arXiv:1903.00735.

[22]. O'Shea, K. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.

[23]. Rahman, J. U., Danish, S., & Lu, D. (2024a). Oscillator simulation with deep neural networks. Mathematics, 12(7), 959.

[24]. Rahman, J. U., Makhdoom, F., & Lu, D. (2023a). Amplifying sine unit: An oscillatory activation function for deep neural networks to recover nonlinear oscillations efficiently. arXiv preprint arXiv:2304.09759.

[25]. Rahman, J. U., Makhdoom, F., & Lu, D. (2023b). ASU-CNN: An efficient deep architecture for image classification and feature visualizations. arXiv preprint arXiv:2305.19146.

[26]. Rahman, J. U., Zulfiqar, R., & Khan, A. (2024b). SwishReLU: A unified approach to activation functions for enhanced deep neural networks performance. arXiv preprint arXiv:2407.08232.

[27]. Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps: Automation of Decision Making, 323-350.

[28]. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.

[29]. Smedsrud, P. H., Thambawita, V., Hicks, S. A., Gjestang, H., Nedrejord, O. O., Næss, E., & Halvorsen, P. (2021). Kvasir- Capsule, a video capsule endoscopy dataset. Scientific Data, 8(1), 142.

[30]. Soydaner, D. (2020). A comparison of optimization algorithms for deep learning. International Journal of Pattern Recognition and Artificial Intelligence, 34(13), 2052013.

[31]. Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., & Pattabiraman, V. (2021). Comparative analysis of deep learning image detection algorithms. Journal of Big data, 8(1), 66.

[32]. Targ, S., Almeida, D., & Lyman, K. (2016). Resnet in resnet: Generalizing residual architectures. arXiv preprint arXiv:1603.08029.

[33]. Ul Rahman, J., Ali, A., Ur Rehman, M., & Kazmi, R. (2020a). A unit softmax with Laplacian smoothing stochastic gradient descent for deep convolutional neural networks. In Intelligent Technologies and Applications: Second International Conference, INTAP 2019, Bahawalpur, Pakistan, November 6–8, 2019, Revised Selected Papers (pp. 162-174). Springer Singapore.

[34]. Ul Rahman, J., Chen, Q., & Yang, Z. (2020b). Additive parameter for deep face recognition. Communications in Mathematics and Statistics, 8, 203-217.

[35]. Ul Rahman, J., Danish, S., & Lu, D. (2023). Deep neural network-based simulation of sel'kov model in glycolysis: A comprehensive analysis. Mathematics, 11(14), 3216.

[36]. Wang, W., Yang, X., Li, X., & Tang, J. (2022). Convolutional capsule network for gastrointestinal endoscopy image classification. International Journal of Intelligent Systems, 37(9), 5796-5815.

[37]. Wang, X., Wang, S., Zhang, S., Fu, T., Shi, H., & Mei, T. (2018). Support vector guided softmax loss for face recognition. arXiv preprint arXiv:1812.11317.

[38]. Yin, P., Zhang, S., Qi, Y., & Xin, J. (2016). Quantization and training of low bit-width convolutional neural networks for object detection. arXiv preprint arXiv:1612.06052.

[39]. Zhang, Z., & Sabuncu, M. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in Neural Information Processing Systems, 31.

[40]. Zhou, J., Jiang, T., Li, Z., Li, L., & Hong, Q. (2019, September). Deep speaker embedding extraction with channel-wise feature responses and additive supervision softmax loss function. In Interspeech (pp. 2883-2887).

[41]. Zinkevich, M., Weimer, M., Li, L., & Smola, A. (2010). Parallelized stochastic gradient descent. Advances in Neural Information Processing Systems, 23, 1-9.

Optimizing Capsule Endoscopy Detection: A Deep Learning Approach with L-Softmax and Laplacian-SGD

Abstract

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