Enhancing Medical Image Classification: A Deep Learning Perspective with Multi Wavelet Transform

Maryam. I.  Al-Khuzaie; Waleed A. Mahmoud  Al-Jawher

doi:10.36371/port.2023.4.7

Authors

Maryam. I. Al-Khuzaie Informatics Institute for Postgraduate Studies, Iraqi Commission for Computer and Informatics, Baghdad, Iraq.
Waleed A. Mahmoud Al-Jawher College of Engineering, Uruk University, Baghdad, Iraq.

Abstract References Downloads Cite Statistics Share Information

Classification of medical images is a very important area of research for both the medical industry and academia. In recent years, automated classification algorithms have become very important in most medical applications, saving time and effort, such as disease detection and diagnostic radiology. Deep learning offers a plethora of advantages when applied to medical image classification, revolutionizing medical diagnosis and patient care. In this study, deep convolutional neural networks (DCNNs) is used to classify medical im-ages and multi-wavelet transform will be applied to extract features. The proposed method aims to improve medical image classification accuracy, thereby assisting healthcare professionals in making more accurate and efficient diagnoses. DCNNs based on the VGG16 model were trained and used in this study. Combining VGG16, a powerful convolutional neural network (CNN), with multiwavelet transform offers several advantages for image processing and analysis tasks, particularly in areas like image classification and feature extraction. To evaluate the performance of the proposed method six publicly available brain tumour MRI datasets are analysed with DCNNs. A fully connected layer is used to categorize the extracted features. According to the results, the deep CNN model combined with the multi-wavelet trans-form achieves an impressive accuracy of 96.43 %. It is evident from this high level of accuracy that the proposed approach is effective in accurately classifying medical images.

Keywords:

Convolutional Neural network, multi-wavelet deep learning, medical image classification

[1] A. Khatami, A. Nazari, A. Beheshti, T. T. Nguyen, S. Nahavandi, and J. Zieba, “Convolutional Neural Network for Medical Image Classification using Wavelet Features,” Proc. Int. Jt. Conf. Neural Networks, 2020, doi: 10.1109/IJCNN48605.2020.9206791.

[2] M. K. Alsmadi, “Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features,” Arab. J. Sci. Eng., vol. 45, no. 4, pp. 3317–3330, 2020, doi: 10.1007/s13369-020-04384-y.

[3] S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” J. Med. Syst., vol. 42, no. 11, pp. 1–13, 2018, doi: 10.1007/s10916-018-1088-1.

[4] Z. Talal Abbood, M. Nasser Al-Turfi, and L. Kamil Adday Almajmaie, “An abbreviated review of deep learning-based image classification models,” Indones. J. Electr. Eng. Comput. Sci., vol. 30, no. 1, p. 491, 2023, doi: 10.11591/ijeecs.v30.i1.pp491-500.

[5] B. C. Ko, S. H. Kim, and J. Y. Nam, “X-ray image classification using random forests with local wavelet-based CS-local binary patterns,” J. Digit. Imaging, vol. 24, no. 6, pp. 1141–1151, Dec. 2011, doi: 10.1007/S10278-011-9380-3.

[6] S. Qasem, S. S.-A. S. Computing, and U. 2011‏, “Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis‏,” Elsevier‏.

[7] B. Krawczyk, G. S.-A. S. Computing, and undefined 2014‏, “A hybrid classifier committee for analysing asymmetry features in breast thermograms‏,” Elsevier‏, Accessed: Aug. 30, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1568494613004109

[8] L. Tsochatzidis, K. Zagoris, N. Arikidis, A. K.-P. Recognition, and undefined 2017‏, “Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach‏,” Elsevier‏, Accessed: Aug. 30, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320317302194

[9] M. I. Razzak, S. Naz, and A. Zaib, “Deep learning for medical image processing: Overview, challenges and the future,” Lect. Notes Comput. Vis. Biomech., vol. 26, pp. 323–350, 2018, doi: 10.1007/978-3-319-65981-7_12.

[10] A. Tuttolomondo, C. Maida, and A. Pinto, “Diabetic Foot Syndrome as a Possible Cardiovascular Marker in Diabetic Patients,” J. Diabetes Res., vol. 2015, p. 268390, 2015, doi: 10.1155/2015/268390.

[11] I. Kiskin, D. Zilli, Y. Li, M. Sinka, K. Willis, and S. Roberts, “Bioacoustic detection with wavelet-conditioned convolutional neural networks,” Neural Comput. Appl., vol. 32, no. 4, pp. 915–927, Feb. 2020, doi: 10.1007/S00521-018-3626-7.

[12] B. Lin, X. Li, W. Y.-F. of Physics, and undefined 2020‏, “Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks‏,” Springer‏, vol. 15, no. 1, p. 12, Apr. 2020, doi: 10.1007/s11467-019-0935-y.

[13] T. Williams, R. L.-I. C. on Learning, and undefined 2018‏, “Wavelet pooling for convolutional neural networks‏,” openreview.net‏, Accessed: Aug. 30, 2022. [Online]. Available: https://openreview.net/forum?id=rkhlb8lCZ

[14] E. Oyallon, … E. B.-P. of the I., and undefined 2017‏, “Scaling the scattering transform: Deep hybrid networks‏,” openaccess.thecvf.com‏, Accessed: Aug. 30, 2022. [Online]. Available: http://openaccess.thecvf.com/content_iccv_2017/html/Oyallon_Scaling_the_Scattering_ICCV_2017_paper.html

[15] H. Lu, H. Wang, Q. Zhang, … D. W.-2018 I. international, and undefined 2018‏, “A dual-tree complex wavelet transform based convolutional neural network for human thyroid medical image segmentation‏,” ieeexplore.ieee.org‏, Accessed: Jul. 03, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8419362/

[16] G. Litjens et al., “A survey on deep learning in medical image analysis‏,” Elsevier‏, Accessed: Jul. 04, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1361841517301135

[17] D. Shen, G. Wu, and H. Il Suk, “Deep Learning in Medical Image Analysis,” Annu. Rev. Biomed. Eng., vol. 19, pp. 221–248, Jun. 2017, doi: 10.1146/ANNUREV-BIOENG-071516-044442.

[18] G. Liu, W. Zhou, and M. Geng, “Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network,” Int. J. Neural Syst., vol. 30, no. 4, Apr. 2020, doi: 10.1142/S0129065719500242.

[19] S. Rajaraman, S. Antani, M. Poostchi, K. S.- PeerJ, and undefined 2018‏, “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images‏,” peerj.com‏, Accessed: Jul. 04, 2023. [Online]. Available: https://peerj.com/articles/4568/?ref=https%3A%2F%2Fgithubhelp.com&utm_source=TrendMD&utm_campaign=PeerJ_TrendMD_1&utm_medium=TrendMD

[20] M. I. Mousa Al-Khuzaay and W. A. Mahmoud Al-Jawher, “New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification,” Int. J. Innov. Comput., vol. 13, no. 1–2, pp. 15–21, 2023, doi: 10.11113/ijic.v13n1-2.414.

[21] H. Sun et al., “Multiwavelet transform and its applications in mechanical fault diagnosis - A review,” Mech. Syst. Signal Process., vol. 43, no. 1–2, pp. 1–24, 2014, doi: 10.1016/j.ymssp.2013.09.015.

[22] X. Wang, Y. Zi, and Z. He, “Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis,” Meas. Sci. Technol., vol. 20, no. 4, p. 045103, Feb. 2009, doi: 10.1088/0957-0233/20/4/045103.

[23] C. Xie and S. Yang, “Construction of refinable function vector via GTST,” Appl. Math. Comput., vol. 194, no. 2, pp. 425–430, Dec. 2007, doi: 10.1016/J.AMC.2006.12.088.

[24] M. Vetterli, C. H.-I. transactions on signal processing, and undefined 1992‏, “Wavelets and filter banks: Theory and design‏,” infoscience.epfl.ch‏, Accessed: Jul. 12, 2023. [Online]. Available: https://infoscience.epfl.ch/record/33904

[25] F. Keinert, Wavelets and Multiwavelets. 2003. doi: 10.1201/9780203011591.

[26] Z. He, H. Cao, Z. Li, Y. Zi, and X. Chen, “The principle of second generation wavelet for milling cutter breakage detection,” Sci. China, Ser. E Technol. Sci., vol. 52, no. 5, pp. 1312–1322, May 2009, doi: 10.1007/S11431-008-0298-Z/METRICS.

[27] J. Yuan, Z. He, and Y. Zi, “Gear fault detection using customized multiwavelet lifting schemes,” Mech. Syst. Signal Process., vol. 24, no. 5, pp. 1509–1528, Jul. 2010, doi: 10.1016/J.YMSSP.2009.11.003.

[28] M. Omran and E. N. Alshemmary, “An Iris Recognition System Using Deep convolutional Neural Network,” J. Phys. Conf. Ser., vol. 1530, no. 1, 2020, doi: 10.1088/1742-6596/1530/1/012159.

[29] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 2015.

[30] J. Deng, W. Dong, R. Socher, L. Li, … K. L.-2009 I. conference, and undefined 2009‏, “Imagenet: A large-scale hierarchical image database‏,” ieeexplore.ieee.org‏, Accessed: Jul. 04, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5206848/

[31] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8689 LNCS, no. PART 1, pp. 818–833, 2014, doi: 10.1007/978-3-319-10590-1_53.

[32] “Brain MRI Images for Brain Tumor Detection | Kaggle.” https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection/code (accessed Jul. 05, 2023).

[33] “Brain MRI | Kaggle.” https://www.kaggle.com/datasets/arabinda91/brain-mri (accessed Jul. 04, 2023).

[34] “Brain_Tumor_Detection_MRI | Kaggle.” https://www.kaggle.com/datasets/abhranta/brain-tumor-detection-mri (accessed Jul. 05, 2023).

[35] “Brain MRI | Kaggle.” https://www.kaggle.com/datasets/arabinda91/brain-mri (accessed Jul. 05, 2023).

[36] “MRI Based Brain Tumor Images | Kaggle.” https://www.kaggle.com/datasets/mhantor/mri-based-brain-tumor-images (accessed Jul. 05, 2023).

[37] “Brain MRI Data | Kaggle.” https://www.kaggle.com/datasets/rizwanulhoqueratul/brain-mri-data (accessed Jul. 05, 2023).

[38] {38] Walid A Mahmoud, Majed E Alneby, Wael H Zayer “2D-multiwavelet transform 2D-two activation function wavelet network-based face recognition” J. Appl. Sci. Res, vol. 6, issue 8, 1019-1028, 2010.

[39] Waleed A Mahmoud, Ali A Ali, & Saad N Abdul Majed “Wavelet Based Multi Carrier Code Division Multiple Access” proceeding of 5th International Multi-Conference on Systems, Signals and Devices, pages 1-6, 2008.

[40] Waleed A Mahmoud, MR Shaker “3D Ear Print Authentication using 3D Radon Trans-form” proceeding of 2nd International Conference on Information & Communication Technologies, Pages 1052-1056, 2006.

[41] Waleed A Mahmoud, Afrah Loay Mohammed Rasheed “3D Image Denoising by Using 3D Multiwavelet” AL-Mustansiriya J. Sci, vol 21, issue 7, pp. 108-136, 2010.

[42] W. A. Mahmoud & I.K. Ibraheem “Image Denoising Using Stationary Wavelet Transform” Signals, Inf. Patt. Proc. & Class., vol 46, issue 4, pp. 1-18, 2003.

[43] WA Mahmoud, MS Abdul-Wahab, AA Sabri “A New Algorithm for Reconstruction of Lost Blocks Using Discrete Wavelet Transform” Engineering and technology journal 24 (10), 2005.

[44] Saleem MR Taha, Walid A Mahmood “New techniques for Daubechies wavelets and multiwavelets implementation using quantum computing” Journal Facta universitatis-series: Electronics and Energetics, Vol. 26, Issue 2, pp. 145-156, 2013.

[45] Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher, “Image Encryption Algorithm Based on Arnold Transform and Chaos Theory in the Multi-wavelet Do-main”, International Journal of Computers and Applications, Vol. 45, Issue 4, pp. 306-322, 2023.

[46] Waleed Ameen Mahmoud Al-Jawher, “Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform” Journal Port Science Research, Volume 4, Issue 2, Pages 111-117, 2021.

[47] Mutaz Abdulwahab and Hadeel Al-Taai Waleed Ameen Mahmoud, “New Fast Method for Computing Wavelet Coefficients from 1D up to 3D” 1st Int. Conference on Digital Communication and computer Application, pp. 313-323, 2007.

[48] Sarah H Awad Waleed A. Mahmoud Al-Jawher, “Precise Classification of Brain Mag-netic Resonance Imaging (MRIs) using Gray Wolf Optimization (GWO)” HSOA Journal of Brain & Neuroscience Research. Vol 6, issue 1, pp 100021, 2022.

[49] Maryam I Mousa Al-Khuzaay, Waleed A Mahmoud Al-Jawher, “New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification” International Journal of Innovative Computing, Volume 13, Issue 1-2, Pages 15-21, 2022.

[50] Hamid M Hasan, Waleed Ameen Mahmoud Al-Jawher and Majid A Alwan, “3-d face recognition using improved 3d mixed transform” International Journal of Biometrics and Bioinformatics (IJBB), Volume 6, Issue 1, Pages 278, 2012.

[51] Waleed A Al-Jawher, Nada N Al-Ramahi, Mikhled. Alfaouri “IMAGE IDENTIFICATION AND LABELING USING HYBRID TRANSFORMATION AND NEURAL NETWORK” Neural Network World: International Journal on Neural and Mass - Parallel Computing and Information Systems, Volume 17, Issue 4, Pages 377-395, 2007.

[52] WA Mahmoud, RA Jassim, “Image Denoising Using Hybrid Transforms” Engineering and Technology Journal, Volume 25, Issue 5, Pages 669-682, 2007.

[53] RA Dihin, E AlShemmary, W Al-Jawher “Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet” Journal of Kufa for Mathematics and Computer 10 (2), 167-172, 2023.

[54] RA Dihin, E AlShemmary, W Al-Jawher “Implementation of The Swin Transformer and Its Application in Image Classification” Journal Port Science Research 6 (4), 318-331, 2023.

[55] AHM Al-Heladi, W. A. Mahmoud, HA Hali, AF Fadhel “Multispectral Image Fusion using Walidlet Transform” Advances in Modelling and Analysis B, vol 52, issue 1-2, pp. 1-20, 2009.

[56] W. A. Mahmoud, J J. Stephan and A. A. Razzak “Facial Expression Recognition Using Fast Walidlet Hybrid Transform” Journal port Science Researchو Volume3, No:1, pages 59-69, 2020.

[57] Waleed Ameen Mahmoud “A Smart Single Matrix Realization of Fast Walidlet Transform” Journal International Journal of Research and Reviews in Computer Sci-ence, Volume 2, Issue, 1, Pages 144-151, 2011.

[58] Abbas Hasan Kattoush, Waleed Ameen Mahmoud Al-Jawher, Sulaiman M Abbas, Ali Tweij Shaheen "A N-Radon Based OFDM Trasceivers Design and Performance Simulation Over Different Channel Models" Wireless Personal Communications, Volume 58, Pages 695-711, 2011.

[59] Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher, ”URUK 4D DISCRETE CHAOTIC MAP FOR SECURE COMMUNICATION APPLICATIONS” Journal Port Science Research, Vol. 5, Issue 3, PP. 131-141, 2023.

[60] Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher, “WAM 3D discrete chaotic map for secure communication applications” International Journal of Innovative Computing, Volume 13, Issue 1-2, Pages 45-54, 2022.

pdf

Al-Khuzaie, M. I. ., & Al-Jawher, W. A. M. . (2023). Enhancing Medical Image Classification: A Deep Learning Perspective with Multi Wavelet Transform. Journal Port Science Research, 6(4), 365–373. https://doi.org/10.36371/port.2023.4.7