Brain Tumor Classification of Medical Images based on Transfer Pre-trained Learning Models
DOI:
https://doi.org/10.31185/wjcms.388Keywords:
Tumor detection, Brain Tumor Detection, GLCM, CNN, Alex Net, Transfer LearningAbstract
Identifying brain tumor types is crucial for accurate diagnoses and appropriate therapeutic planning in medical imaging. The study aims to develop a robust and accurate system for categorizing medical images of brain tumors, utilizing transfer learning from pre-trained neural network models. The purpose is to optimize the efficiency and precision of brain tumor diagnosis, ultimately facilitating prompt and precise medical intervention by clinicians. This endeavor holds immense importance in promptly diagnosing and treating brain tumors. Transfer learning greatly facilitates the classification of different forms of brain tumors visible in medical imaging, enabling the achievement of this target. Utilizing pre-trained neural network models capitalizes on their comprehension of visual attributes and patterns by adjusting to the specific activity with efficiency and accuracy. Through this strategy, the training process is accelerated, and the model is generalized. Medical professionals can rapidly and reliably diagnosing brain tumors from medical imaging via this technique. he results demonstrate that the AlexNet model showed a remarkable performance, with an accuracy rate of 95.60% and high sensitivity and specificity rates.
Downloads
References
[1] S. N. Sivanandam and S. N. Deepa, "Introduction to Genetic Algorithms," Springer-Verlag, Berlin, Heidelberg, 2008.
[2] O. H. Babatunde, L. Armstrong, J. Leng, and D. Diepeveen, "A genetic algorithm-based feature selection," 2014.
[3] Y. M. Y. Abdallah and T. Alqahtani, "Research in medical imaging using image processing techniques," in Medical Imaging-Principles and Applications, IntechOpen, 2019.
[4] Y. Abdallah, "Improvement of sonographic appearance using HATTOP methods," International Journal of Science and Research (IJSR), vol. 4, no. 2, pp. 2425-2430, 2015.
[5] P. Ganesan and G. Sajiv, "A comprehensive study of edge detection for image processing applications," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 2017. DOI: https://doi.org/10.1109/ICIIECS.2017.8275968
[6] V. A. Pimpalkhute et al., "Digital image noise estimation using DWT coefficients," IEEE Transactions on Image Processing, vol. 30, pp. 1962-1972, 2021. DOI: https://doi.org/10.1109/TIP.2021.3049961
[7] S. Motamed, P. Rogalla, and F. Khalvati, "Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images," Informatics in Medicine Unlocked, vol. 27, p. 100779, 2021. DOI: https://doi.org/10.1016/j.imu.2021.100779
[8] H. L. Halme et al., "Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images," EJNMMI Research, vol. 12, no. 1, p. 27, 2022. DOI: https://doi.org/10.1186/s13550-022-00897-9
[9] H. Nematzadeh et al., "Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices," Optics and Lasers in Engineering, vol. 110, pp. 24-32, 2018. DOI: https://doi.org/10.1016/j.optlaseng.2018.05.009
[10] M. Arif and G. Wang, "Fast curvelet transform through genetic algorithm for multimodal medical image fusion," Soft Computing, vol. 24, no. 3, pp. 1815-1836, 2020. DOI: https://doi.org/10.1007/s00500-019-04011-5
[11] D. J. Hemanth and J. Anitha, "Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images," Applied Soft Computing, vol. 75, pp. 21-28, 2019. DOI: https://doi.org/10.1016/j.asoc.2018.10.054
[12] N. B. Bahadure, A. K. Ray, and H. P. Thethi, "Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm," Journal of Digital Imaging, vol. 31, no. 4, pp. 477-489, 2018. DOI: https://doi.org/10.1007/s10278-018-0050-6
[13] P. Liu et al., "Deep evolutionary networks with expedited genetic algorithms for medical image denoising," Medical Image Analysis, vol. 54, pp. 306-315, 2019. DOI: https://doi.org/10.1016/j.media.2019.03.004
[14] N. K. Pareek and V. Patidar, "Medical image protection using genetic algorithm operations," Soft Computing, vol. 20, no. 2, pp. 763-772, 2014. DOI: https://doi.org/10.1007/s00500-014-1539-7
[15] M. A. Mohammed et al., "A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear," Future Generation Computer Systems, vol. 89, pp. 539-547, 2018. DOI: https://doi.org/10.1016/j.future.2018.07.022
[16] T. Peirelinck et al., "Transfer learning in demand response: A review of algorithms for data-efficient modelling and control," Energy and AI, vol. 7, p. 100126, 2022. DOI: https://doi.org/10.1016/j.egyai.2021.100126
[17] D. R. Sarvamangala and R. V. Kulkarni, "Convolutional neural networks in medical image understanding: a survey," Evolutionary Intelligence, vol. 15, no. 1, pp. 1-22, 2022. DOI: https://doi.org/10.1007/s12065-020-00540-3
[18] Y. Xu, X. Zhao, and J. Gong, "A Large-Scale Secure Image Retrieval Method in Cloud Environment," IEEE Access, pp. 1-1, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2951175
[19] A. Rehman et al., "A deep learning-based framework for automatic brain tumors classification using transfer learning," Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 757-775, 2020. DOI: https://doi.org/10.1007/s00034-019-01246-3
[20] Z. Huang et al., "Projection metric learning on Grassmann manifold with application to video based face recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 140-149. DOI: https://doi.org/10.1109/CVPR.2015.7298609
[21] A. Alzu’bi, A. Amira, and N. Ramzan, "Semantic content-based image retrieval: A comprehensive study," Journal of Visual Communication and Image Representation, vol. 32, pp. 20-54, 2015. DOI: https://doi.org/10.1016/j.jvcir.2015.07.012
[22] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
[23] J. Deng, "A large-scale hierarchical image database," in Proc. of IEEE Computer Vision and Pattern Recognition, 2009, pp. 2009. DOI: https://doi.org/10.1109/CVPR.2009.5206848
[24] M. I. Razzak, M. Imran, and G. Xu, "Big data analytics for preventive medicine," Neural Comput. Appl., 2019. DOI: https://doi.org/10.1007/s00521-019-04095-y
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Mohamed H Ghaleb Abdkhaleq Abdkhaleq, Ibraheem Al-Jadir, Loay E. George, Gromov, Yuri Yuryevich, Eliseev Aleksey Igorevich
This work is licensed under a Creative Commons Attribution 4.0 International License.
