Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

KİRAZ, AHMET HİDAYET; OUMAR DJIBRILLAH, Fatime; YÜKSEL, Mehmet Emin

doi:10.55730/1300-0632.4037

Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

Ahmet Hidayet KİRAZ, (Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Biyomedikal Mühendisliği Anabilim Dalı, Kayseri, Türkiye)

Fatime OUMAR DJIBRILLAH, (Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Biyomedikal Mühendisliği Anabilim Dalı, Kayseri, Türkiye)

Mehmet Emin YÜKSEL (Erciyes Üniversitesi, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü, Kayseri, Türkiye)

Turkish Journal of Electrical Engineering and Computer Sciences

10 0

Yıl: 2023 Cilt: 31 Sayı: SI-1 (6) Sayfa Aralığı: 1113 - 1128 Metin Dili: İngilizce DOI: 10.55730/1300-0632.4037 İndeks Tarihi: 22-11-2023

Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

Öz:

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, KNN, linear DA, and ANN) for the classification purpose. The study is performed to conduct a comparative analysis, aiming to evaluate the individual impact of each step within the proposed methodology and also assess the performance of each implemented classifier in order to find a best pipeline for medical image classification. The effectiveness of the proposed approach is assessed using two different medical image datasets. The performance assessment for the classifiers implemented is achieved using overall accuracy, sensitivity, and specificity metrics. The findings show that the linear DA classifier preceded by deep feature extraction, concatenation, and dimensionality reduction reveals itself to be a very eﬀicient pipeline for accurate classification of medical images by utilizing a very small number of features.

Anahtar Kelime: Medical images classification deep feature extraction concatenation dimensionality reduction autoencoder

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering 2017;19:221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442
[2] Anwar S, Majid M, Qayyum A, Awais M, Alnowami M et al. Medical image analysis using convolutional neural networks: a review. Journal of Medical Systems 2018; 42:1–13. https://doi.org/10.1007/s10916-018-1088-1.
[3] Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimedia tools and applications 2021; 80:24365–24398. https://doi.org/10.1007/s11042-021-10707-4.
[4] Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3-21. https://doi.org/10.1007/978-3-030-33128-_1
[5] Bakator M, Radosav D. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction. 2018; 2 (3):47. https://doi.org/10.3390/mti2030047
[6] Razzak M, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps: Automation of Decision Making 2018:323–350. https://doi.org/10.1007/978-3-319-65981- 7_12
[7] Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Zeitschrift fur Medizinische Physik 2019; 29 (2):86–101. https://doi.org/10.1016/j.zemedi.2018.12.003
[8] Lundervold A, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur Medizinische Physik 2019; 29 (2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
[9] Altaf F, Islam S, Akhtar N, Janjua N. Going deep in medical image analysis: concepts, methods, challenges, and future directions. IEEE Access 2019; 7:99540–99572. https://doi.org/10.1109/ACCESS.2019.2929365
[10] Singh A, Sengupta S, Lakshminarayanan V. Explainable deep learning models in medical image analysis. Journal of Imaging 2020; 6 (6):52. https://doi.org/10.3390/jimaging6060052
[11] Liu T, Siegel E, Shen D. Deep learning and medical image analysis for COVID-19 diagnosis and prediction. Annual Review of Biomedical Engineering 2022; 24:179–201. https://doi.org/10.1146/annurev-bioeng-110220-012203.
[12] Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval 2022; 11 (1):19–38. https://doi.org/10.1007/s13735-021-00218-1
[13] Wang L, Wang H, Huang Y, Yan B, Chang Z et al. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020. European Journal of Radiology 2022; 146:110069. https://doi.org/10.1016/j.ejrad.2021.110069
[14] Belfin RV, Anitha J, Nainan A, Thomas L. An Eﬀicient Approach for Brain Tumor Detection Using Deep Learning Techniques. In: International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021; 1:297-312. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_25
[15] Anitha J, Ting I, Agnes SA, Pandian IA, Belfin RV. Social media data analytics using feature engineering. In: Systems Simulation and Modeling for Cloud Computing and Big Data Applications (Peter J D and Fernandes S L, Editors), 29-59. Academic Press, 2020.
[16] Devi SS, Roy A, Singha J et al. Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimed Tools Appl 77 2018; 631–660. https://doi.org/10.1007/s11042- 016-4264-7
[17] Rabidas R, Laishram R, Roy A. Benign–Malignant Mass Characterization Based on Multi-gradient Quinary Pat- terns. In: Agrawal R, Kishore Singh C, Goyal A. (eds) Advances in Smart Communication and Imaging Systems; Lecture Notes in Electrical Engineering, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-15-9938-5_1
[18] Jogin M, Madhulika MS, Divya GD, Meghana RK, Apoorva S et al. Feature extraction using con- volution neural networks (CNN) and deep learning. In: 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT); 2018; 2319–2323. https://doi.org/10.1109/RTEICT42901.2018.9012507
[19] Lai Z, Deng H. Medical Image Classification Based on Deep Features Extracted by Deep Model and Statis- tic Feature Fusion with Multilayer Perceptron. Computational Intelligence and Neurosciences 2018;2018:2061516. https://doi.org/10.1155/2018/2061516
[20] Sungheetha A, Sharma R. Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. Journal of Trends in Computer Science and Smart Technology (TCSST) 2021; 3 (2):81–94. https://doi.org/10.36548/jtcsst.2021.2.002
[21] Yang A, Yang X, Wu W, Liu H, Zhuansun Y. Research on feature extraction of tumor image based on convolutional neural network. IEEE access 2019; 7:24204–24213.
[22] Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A. Pneumonia detection using CNN based feature extraction. In 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT) 2019; 1–7. https://doi.org/10.1109/ICECCT.2019.8869364
[23] Sahlol A, Yousri D, Ewees A, Al-Qaness M, Damasevicius R et al. COVID-19 image classification us- ing deep features and fractional-order marine predators algorithm. Scientific reports 2020; 10 (1):1–15. https://doi.org/10.1038/s41598-020-71294-2
[24] Ayesha S, Hanif M, Talib R. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion 2020; 59:44–58. https://doi.org/10.1016/j.inffus.2020.01.005
[25] Mateen M, Wen J, Song S, Huang Z. Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry 2019; 11 (1):1. https://doi.org/10.3390/sym11010001
[26] Gunduz H. An eﬀicient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson’s disease classification. Biomedical Signal Processing and Control 2021; 66:102452. https://doi.org/10.1016/j.bspc.2021.102452
[27] Hasan B, Abdulazeez A. A review of principal component analysis algorithm for dimensionality reduction. Journal of Soft Computing and Data Mining 2021; 2 (1):20–30. https://doi.org/10.30880/JSCDM.2021.02.01.003
[28] Islam M, Xing L. A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data. Nature Biomedical Engineering 2021; 5 (6):624–635. A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data.
[29] Atasever S, Azginoglu N, Terzi D, Terzi R. A comprehensive survey of deep learning research on medical image anal- ysis with focus on transfer learning. Clinical Imaging 2023; 94:18-41. https://doi.org/10.1016/j.clinimag.2022.11.003
[30] Kandel I, Castelli M. Transfer learning with convolutional neural networks for diabetic retinopathy image classifi- cation. A review. Applied Sciences 2020; 10 (6):2021. https://doi.org/10.3390/app10062021.
[31] Shaha M, Pawar M. Transfer learning for image classification. In: 2018 second international conference on electronics, communication and aerospace technology (ICECA); Coimbatore, India; 2018. pp. 656-660.
[32] Morid M, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using Im- ageNet. Computers in biology and medicine 2021; 128:104115. https://doi.org/10.1016/j.compbiomed.2020.104115
[33] Panigrahi S, Nanda B, Bhuyan R, Kumar K, Ghosh S et al. Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning. Heliyon 2023; 9 (3). https://doi.org/10.1016/j.heliyon.2023.e13444
[34] Morís DI, Hervella ÁS, Rouco J, Novo J, Ortega M. Context encoder transfer learning approaches for retinal image analysis. Comput Biol Med. 2023; 152:106451. https://doi.org/10.1016/j.compbiomed.2022.106451
[35] Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal. 2021; 72:102125. https://doi.org/10.1016/j.media.2021.102125
[36] Dara S, Tumma P, Eluri N, Kancharla G. Feature extraction in medical images by using deep learning approach. International Journal of Pure and Applied Mathematics 2018; 120 (6):305–312.
[37] Virmani J, Agarwal R. Deep feature extraction and classification of breast ultrasound images. Multimedia Tools and Applications 2020; 79 (37-38):27257–27292. https://doi.org/10.1007/s11042-020-09337-z
[38] Saad W, Shalaby WA, Shokair M, El-Samie FA, Dessouky M et al. COVID-19 classification using deep feature con- catenation technique. J Ambient Intell Humaniz Comput. 2022;13 (4):2025-2043.https://doi.org/10.1007/s12652- 021-02967-7
[39] Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M et al. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 2020; 8:55135–55144. https://doi.org/10.1109/ACCESS.2020.2978629
[40] Nguyen L, Lin D, Lin Z, Cao J. Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS); Florence, Italy, 2018, pp. 1-5,https://doi.org/10.1109/ISCAS.2018.8351550
[41] Rajinikanth V, Joseph Raj AN, Thanaraj KP, Naik GR. A Customized VGG19 Network with Concatena- tion of Deep and Handcrafted Features for Brain Tumor Detection. Applied Sciences. 2020; 10 (10):3429. https://doi.org/10.3390/app10103429
[42] Liang X, Hu P, Zhang L, Sun J, Yin G. MCFNet: Multi-layer concatenation fusion network for medical images fusion. IEEE Sensors Journal 2019; 19 (16):7107–7119. https://doi.org/10.1109/JSEN.2019.2913281
[43] NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories. https://academictorrents.com/details/557481faacd824c83fbf57dcf7b6da9383b3235a.
[44] Wang X, Peng Y, Lu L, Lu Z, Bagheri M et al. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017; pp. 2097–2106.
[45] preprocessed_eye_diseases_fundus_images. https://www.kaggle.com/datasets/gunavenkatdoddi/preprocessed- eye-diseases-fundus-images.
[46] Le Q, others. A tutorial on deep learning part 1: Nonlinear classifiers and the backpropagation algorithm. Mountain View, CA 2015.
[47] Hao X, Zhang G, Ma S. Deep learning. International Journal of Semantic Computing 2016; 10 (3):417–439. https://doi.org/10.1142/S1793351X16500045
[48] O’Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 2015. https://doi.org/10.48550/arXiv.1511.08458
[49] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y et al. (2021). Review of deep learning: Concepts, CNN ar- chitectures, challenges, applications, future directions. Journal of big Data, 8:1-74. https://doi.org/10.1186/s40537- 021-00444-8
[50] Wu J. Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China 2017; 5 (23):495.
[51] Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Communi- cations of the ACM 2017; 60 (6):84–90. https://doi.org/10.1145/3065386
[52] Elhassouny A, Smarandache F. Trends in deep convolutional neural Networks architectures: A review. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE); Agadir, MoroccoÜ 2019, pp. 1-8, https://doi.org/10.1109/ICCSRE.2019.8807741
[53] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S et al. Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR); Boston, MA, USA; 2015, pp. 1–9. https://doi.org/10.1109/CVPR.2015.7298594
[54] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014. https://doi.org/10.48550/arXiv.1409.1556
[55] Altuntaş Y, KOCAMAZ F. Deep feature extraction for detection of tomato plant diseases and pests based on leaf images. Celal Bayar University Journal of Science 2021; 17 (2):145–157. https://doi.org/ 10.18466/cbayarfbe.812375
[56] Bank D, Koenigstein N, Giryes R. Autoencoders. arXiv preprint arXiv:2003.05991 2020. https://doi.org/10.48550/arXiv.2003.05991
[57] Meng Q, Catchpoole D, Skillicom D, Kennedy P. Relational autoencoder for feature extraction. In 2017 International joint conference on neural networks (IJCNN); Anchorage, AK; 2017, pp. 364–371. https://doi.org/10.1109/IJCNN.2017.7965877

APA	KİRAZ A, OUMAR DJIBRILLAH F, YÜKSEL M (2023). Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. , 1113 - 1128. 10.55730/1300-0632.4037
Chicago	KİRAZ AHMET HİDAYET,OUMAR DJIBRILLAH Fatime,YÜKSEL Mehmet Emin Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. (2023): 1113 - 1128. 10.55730/1300-0632.4037
MLA	KİRAZ AHMET HİDAYET,OUMAR DJIBRILLAH Fatime,YÜKSEL Mehmet Emin Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. , 2023, ss.1113 - 1128. 10.55730/1300-0632.4037
AMA	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. . 2023; 1113 - 1128. 10.55730/1300-0632.4037
Vancouver	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. . 2023; 1113 - 1128. 10.55730/1300-0632.4037
IEEE	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M "Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models." , ss.1113 - 1128, 2023. 10.55730/1300-0632.4037
ISNAD	KİRAZ, AHMET HİDAYET vd. "Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models". (2023), 1113-1128. https://doi.org/10.55730/1300-0632.4037

APA	KİRAZ A, OUMAR DJIBRILLAH F, YÜKSEL M (2023). Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. Turkish Journal of Electrical Engineering and Computer Sciences, 31(SI-1 (6)), 1113 - 1128. 10.55730/1300-0632.4037
Chicago	KİRAZ AHMET HİDAYET,OUMAR DJIBRILLAH Fatime,YÜKSEL Mehmet Emin Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. Turkish Journal of Electrical Engineering and Computer Sciences 31, no.SI-1 (6) (2023): 1113 - 1128. 10.55730/1300-0632.4037
MLA	KİRAZ AHMET HİDAYET,OUMAR DJIBRILLAH Fatime,YÜKSEL Mehmet Emin Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. Turkish Journal of Electrical Engineering and Computer Sciences, vol.31, no.SI-1 (6), 2023, ss.1113 - 1128. 10.55730/1300-0632.4037
AMA	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. Turkish Journal of Electrical Engineering and Computer Sciences. 2023; 31(SI-1 (6)): 1113 - 1128. 10.55730/1300-0632.4037
Vancouver	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models. Turkish Journal of Electrical Engineering and Computer Sciences. 2023; 31(SI-1 (6)): 1113 - 1128. 10.55730/1300-0632.4037
IEEE	KİRAZ A,OUMAR DJIBRILLAH F,YÜKSEL M "Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models." Turkish Journal of Electrical Engineering and Computer Sciences, 31, ss.1113 - 1128, 2023. 10.55730/1300-0632.4037
ISNAD	KİRAZ, AHMET HİDAYET vd. "Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models". Turkish Journal of Electrical Engineering and Computer Sciences 31/SI-1 (6) (2023), 1113-1128. https://doi.org/10.55730/1300-0632.4037