Binet Rose Devassy; Jobin K. Antony; Dominic Mathew

Optimized Octave Convolution Network Model for Histopathological Image Classification

PDF (1493KB), PP.94-104

Views: 0 Downloads: 0

Author(s)

Binet Rose Devassy ^1,2,* Jobin K. Antony ³ Dominic Mathew ⁴

1. APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India

2. Department of Electronics and Communication Engineering, Sahrdaya College of Engineering and Technology, Thrissur, Kerala, India

3. Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

4. Department of Applied Electronics and Instrumentation, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2025.06.07

Received: 10 Jun. 2025 / Revised: 21 Aug. 2025 / Accepted: 28 Sep. 2025 / Published: 8 Dec. 2025

Index Terms

Histopathological Image, Octave Convolution, High and Low Frequency Feature Extraction, Spatial Down Sampling

Abstract

Accurate histopathological image classification plays a crucial role in cancer detection and diagnosis. In automated cancer detection methods, extraction of histological features of malignant and benign tissues is a challenging task. This paper presents a modified approach on octave convolution to extract high and low-frequency features which help to provide a comprehensive representation of histopathological images. Proposed octave convolution model is used to perform histopathological image classification using three different optimization strategies. Firstly, an optimal alpha value of 0.5 is used to give equal importance to both high-frequency and low-frequency feature maps. This balanced approach ensures that the model effectively considers critical high-frequency features as well as low-frequency features of cancerous tissues. Secondly, high-frequency and low-frequency feature maps are extracted and down sampled into half the spatial dimension size to reduce the computational cost compared to standard CNN. Thirdly, training and validation was conducted using ReLU, PReLU, LeakyReLU, ELU, GELU and Swish activation functions. From the experiment, it was concluded that PReLU is the best activation function for capturing intricate patterns inherent in cancer-related histopathological images. Combining all these optimization strategies, the proposed method proved to provide a classification accuracy of 93% and also to reduce the computational cost by 50%. Performance validation against pre-trained models, CNN variants and vision transformer-based models has also been conducted, which proved superior performance of the proposed model.

Cite This Paper

Binet Rose Devassy, Jobin K. Antony, Dominic Mathew, "Optimized Octave Convolution Network Model for Histopathological Image Classification", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.6, pp.94-104, 2025. DOI:10.5815/ijisa.2025.06.07

Reference

[1]Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. “Global cancer statistics 2020: GLOBOCAN es timates of incidence and mortality worldwide for 36 cancers in 185 countries”. CA Cancer J Clin 2021;71:209-49.
[2]Tabatabaei, Z.; Wang, Y.; Colomer, A.; Oliver Moll, J.; Zhao, Z.; Naranjo, V. “WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval”, Bioengineering 2023, 10, 1144. https://doi.org/10.3390/bioengineering10101144
[3]Lowe, D.G., “Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94
[4]F. Ullah, M. Nadeem, M. Abrar, "Revolutionizing Brain Tumor Segmentation in MRI with Dynamic Fusion of Handcrafted Features and Global Pathway-based Deep Learning," KSII Transactions on Internet and Information Systems, vol. 18, no. 1, pp. 105-125, 2024. DOI: 10.3837/tiis.2024.01.007.
[5]ŞabanÖztürk, BayramAkdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA”, Procedia Computer Science, Volume 132, 2018, Pages 40-46, ISSN 1877- 0509, https://doi.org/10.1016/j.procs.2018.05.057.
[6]Ullah F, Nadeem M, Abrar M, Al-Razgan M, Alfakih T, Amin F, Salam A. “Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network. Diagnostics”. 2023; 13(16):2650. https://doi.org/10.3390/diagnostics13162650
[7]Ahmed S, Shaikh A, Alshahrani H, Alghamdi A, Alrizq M, Baber J, Bakhtyar M. “Transfer Learning Approach for Classification of Histopathology Whole Slide Images”, Sensors (Basel). 2021 Aug 9;21(16):5361. doi: 10.3390/s21165361. PMID: 34450802; PMCID: PMC8401188.
[8]Rakhlin, A., Shvets, A., Iglovikov, V., Kalinin, A.A. (2018), “Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis”. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_83
[9]Muhammed Talo, “Automated classification of histopathology images using transfer learning”, Artificial Intelligence in Medicine, Volume 101, 2019, 101743, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2019.101743.
[10]R. W. Anwar, M. Abrar and F. Ullah, "Transfer Learning in Brain Tumor Classification: Challenges, Opportunities, and Future Prospects," 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, 2023, pp. 24-29, doi: 10.1109/ICTC58733.2023.10392830
[11]Kosaraju, S., Park, J., Lee, H. et al. “Deep learning-based framework for slide-based histopathological image analysis”. Sci Rep 12, 19075 (2022). https://doi.org/10.1038/s41598-022-23166-0
[12]Gour, Mahesh et al. “Residual learning based CNN for breast cancer histopathological image classification.” Int. J. Imaging Syst. Technol. 30 (2020): 621-635.
[13]Devassy, B.R., Antony, J.K. (2022). “Histopathological Image Classification Using Ensemble Transfer Learning”. In: Misra, R., Shyamasundar, R.K., Chaturvedi, A., Omer, R. (eds) Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021). ICMLBDA 2021. Lecture Notes in Networks and Systems, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-030-82469-3_18
[14]Ullah, Faizan & Nadeem, Muhammad & Abrar, Muhammad & Amin, Farhan & Salam, Abdu & Alabrah, Amerah & AlSalman, Hussain. (2023). “Evolutionary Model for Brain Cancer-Grading and Classification”. IEEE Access. PP. 1-1. 10.1109/ACCESS.2023.3330919.
[15]J. Hu, L. Shen and G. Sun, "Squeeze-and-Excitation Networks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 7132-7141, doi: 10.1109/CVPR.2018.00745.
[16]Devassy, B.R., Antony, J.K. “Histopathological image classification using CNN with squeeze and excitation networks based on hybrid squeezing”. SIViP 17, 3613–3621 (2023). https://doi.org/10.1007/s11760-023-02587-y
[17]Y. Chen et al., "Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 3434-3443, doi: 10.1109/ICCV.2019.00353.
[18]https://doi.org/10.48550/arXiv.2104.05345
[19]Dhar P, Garg VK, Rahman MA, “ Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals”. J Healthc Eng. 2022 Mar 16;2022:3491828. doi: 10.1155/2022/3491828. Retraction in: J Healthc Eng. 2023 May 24;2023:9825862. PMID: 35340257; PMCID: PMC8942662.
[20]F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264
[21]Lukic V, Brüggen M. “Galaxy Classifications with Deep Learning”. Proceedings of the International Astronomical Union. 2016;12(S325):217-220. doi:10.1017/S1743921316012771
[22]arXiv:1502.01852 https://doi.org/10.48550/arXiv.1502.01852
[23]Abimouloud, M.L., Bensid, K., Elleuch, M. et al. “Vision transformer based convolutional neural network for breast cancer histopathological images classification”. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19667-x
[24]Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”. https://arxiv.org/abs/2010.11929 .
[25]Ullah F, Nadeem M, Abrar M, Amin F, Salam A, Khan S. “Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures”. Mathematics. 2023;11(19):4189. https://doi.org/10.3390/math11194189

International Journal of Intelligent Systems and Applications (IJISA)