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Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm

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Abstract

The disease that causes a large number of deaths annually across the world is brain cancer and it has become an important research topic in the field of medical image processing in recent times. There are various techniques for the detection of brain tumors (BT) but magnetic resonance imaging (MRI) diagnosing techniques show superior performance in the prognosis and examination of brain tumors in the early stages. The manual detection of brain tumors by radiologists leads to many limitations like errors and lack of detection accuracy. Hence, there is a need for computer-aided diagnostic techniques to help radiologists in detecting brain tumors accurately from the MRI images. To make this process more effective, the implementation of an automated technique is a preferred choice. In this paper, an effective detection and classification technique Adaptive convolutional Autoencoder-based Snow Avalanches (ACAE-SA) Algorithm is proposed. This algorithm comprises an Adaptive CNN component and an Autoencoder to detect and categorize BT from the MRI images. To mitigate the computational complexities in these components a Snow Avalanches algorithm is integrated into this work as an optimization technique. For the validation of the proposed architecture two MRI image datasets namely figshare and BraTS 2018 are used. The proposed technique proved its effectiveness in the detection and classification of brain tumors from the MRI images and outperformed the state-of-the-art techniques.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Mohan, P., Veerappampalayam Easwaramoorthy, S., Subramani, N., Subramanian, M., Meckanzi, S.: Handcrafted deep-feature-based brain tumor detection and classification using mri images. Electronics 11(24), 4178 (2022)

    Article  Google Scholar 

  2. Saravanan, S., Kumar, V.V., Sarveshwaran, V., Indirajithu, A., Elangovan, D., Allayear, S.M.: Computational and mathematical methods in medicine glioma brain tumor detection and classification using convolutional neural network. Comput. Math. Methods Med. (2022)

  3. Maqsood, S., Damaševičius, R., Maskeliūnas, R.: Multi-modal brain tumor detection using deep neural network and multiclass SVM. Medicina 58(8), 1090 (2022)

    Article  Google Scholar 

  4. Lamrani, D., Cherradi, B., El Gannour, O., Bouqentar, M.A., Bahatti, L.: Brain tumor detection using mri images and convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 13(7) (2022).

  5. Saeedi, S., Rezayi, S., Keshavarz, H.R., Niakan Kalhori, S.: MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med. Inf. Decision Making 23(1), 16 (2023)

    Article  Google Scholar 

  6. Uzun Ozsahin, D., Onakpojeruo, E.P., Uzun, B., Mustapha, M.T., Ozsahin, I.: Mathematical assessment of machine learning models used for brain tumor diagnosis. Diagnostics 13(4), 618 (2023)

    Article  Google Scholar 

  7. Aleid, A., Alhussaini, K., Alanazi, R., Altwaimi, M., Altwijri, O., Saad, A.S.: Artificial intelligence approach for early detection of brain tumors using MRI images. Appl. Sci. 13(6), 3808 (2023)

    Article  Google Scholar 

  8. Senan, E.M., Jadhav, M.E., Rassem, T.H., Aljaloud, A.S., Mohammed, B.A., Al-Mekhlafi, Z.G.: Early diagnosis of brain tumour mri images using hybrid techniques between deep and machine learning. Comput. Math. Methods Med. (2022)

  9. Mehrotra, R., Ansari, M.A., Agrawal, R., Anand, R.S.: A transfer learning approach for AI-based classification of brain tumors. Mach. Learn. Appl. 2, 100003 (2020)

    Google Scholar 

  10. Jena, B., Saxena, S., Nayak, G.K., Balestrieri, A., Gupta, N., Khanna, N.N., Laird, J.R., Kalra, M.K., Fouda, M.M., Saba, L., Suri, J.S.: Brain tumor characterization using radiogenomics in artificial intelligence framework. Cancers 14(16), 4052 (2022)

    Article  Google Scholar 

  11. ZainEldin, H., Gamel, S.A., El-Kenawy, E.S.M., Alharbi, A.H., Khafaga, D.S., Ibrahim, A., Talaat, F.M.: Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering 10(1), 18 (2022)

    Article  Google Scholar 

  12. Amin, J., Sharif, M., Yasmin, M., Fernandes, S.L.: A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn. Lett. 139, 118–127 (2020)

    Article  Google Scholar 

  13. Yin, B., Wang, C., Abza, F.: New brain tumor classification method based on an improved version of whale optimization algorithm. Biomed. Signal Process. Control 56, 101728 (2020)

    Article  Google Scholar 

  14. Arif, M., Ajesh, F., Shamsudheen, S., Geman, O., Izdrui, D., Vicoveanu, D.: Brain tumor detection and classification by MRI using biologically inspired orthogonal wavelet transform and deep learning techniques. J. Healthcare Eng. (2022)

  15. Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., Ahmad, I.S., Kamhi, S.: Brain tumor detection and classification on MR images by a deep wavelet auto-encoder model. Diagnostics 11(9), 1589 (2021)

    Article  Google Scholar 

  16. Ait-Amou, M., Xia, K., Kamhi, S., Mouhafid, M.: A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization. Healthcare 10(3), 494 (2022)

    Article  Google Scholar 

  17. Quang, S. L. Brats-2018. Kaggle. https://www.kaggle.com/datasets/sanglequang/brats2018 (2020 3).

  18. Rathee, A. Figshare Brain Tumor Dataset. Kaggle. https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset (2022, March 23).

  19. Ojo, M.O., Zahid, A.: Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agronomy 13(3), 887 (2023)

    Article  Google Scholar 

  20. Jameel, S.M., Hashmani, M.A., Rehman, M., Budiman, A.: Adaptive CNN ensemble for complex multispectral image analysis. Complexity, pp. 1–21 (2020)

  21. Ferreira, D., Silva, S., Abelha, A., Machado, J.: Recommendation system using autoencoders. Appl. Sci. 10(16), 5510 (2020)

    Article  Google Scholar 

  22. Golalipour, K., Nowdeh, S.A., Akbari, E., Hamidi, S.S., Ghasemi, D., Abdelaziz, A.Y., Yousef, A.: Snow avalanches algorithm (SAA): a new optimization algorithm for engineering applications. Alex. Eng. J. 83, 257–285 (2023)

    Article  Google Scholar 

  23. Alagarsamy, S., Kamatchi, K., Govindaraj, V., Zhang, Y.D., Thiyagarajan, A.: Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors. Biocybern. Biomed. Eng. 39(4), 1005–1035 (2019)

    Article  Google Scholar 

  24. Nisha, A.V., Rajasekaran, M.P., Kottaimalai, R., Vishnuvarthanan, G., Arunprasath, T., Muneeswaran, V.: Hybrid D-OCapNet: automated multi-class Alzheimer’s disease classification in brain MRI using hybrid dense optimal capsule network. Int. J. Pattern Recogn. Artific. Intell. (2023).

  25. Natarajan, S., Govindaraj, V., Murugan, P.R., Zhang, Y., Thiyagarajan, A.P., Uma, K.: Tumour region detection in MR brain images using MFCM based segmentation and Self Accommodative JAYA based optimization. In 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) (pp. 340–345). IEEE (2023).

  26. Alagarsamy, S., Govindaraj, V.: Automated brain tumor segmentation for MR brain images using artificial bee colony combined with interval type-II fuzzy technique. IEEE Trans. Ind. Inform. (2023).

  27. Wang, S.H., Govindaraj, V.V., Górriz, J.M., Zhang, X., Zhang, Y.D.: Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inform. Fusion 67, 208–229 (2021)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mohamed Sathak Engineering College, Kilakarai, Tamil Nadu, India

    E. Dhiravidachelvi

  2. School of Computer Science and Engineering, VIT University, Vellore, India

    T. Joshva Devadas

  3. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

    P. J. Sathish Kumar

  4. Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India

    S. Senthil Pandi

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  1. E. Dhiravidachelvi
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  2. T. Joshva Devadas
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  3. P. J. Sathish Kumar
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  4. S. Senthil Pandi
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dhiravidachelvi E, Joshva Devadas T, Sathish Kumar P.J, Senthil Pandi S. The first draft of the manuscript was written by Dhiravidachelvi E and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to E. Dhiravidachelvi.

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Dhiravidachelvi, E., Devadas, T.J., Kumar, P.J.S. et al. Enhancing image classification using adaptive convolutional autoencoder-based snow avalanches algorithm. SIViP 18, 6867–6879 (2024). https://doi.org/10.1007/s11760-024-03357-0

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