Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection

Biocybernetics and Biomedical Engineering - Tập 42 - Trang 646-666 - 2022
Mesut Toğaçar1, Burhan Ergen2, Vedat Tümen3
1Computer Technologies Department, Technical Sciences Vocational School, Fırat University Elazığ, Turkey
2Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey
3Department of Computer Engineering, Faculty of Engineering and Architecture, Bitlis Eren University Bitlis, Turkey

Tài liệu tham khảo

Ibrahim, 2020, A hybrid computer-aided diagnosis of retinopathy by optical coherence tomography integrating machine learning and feature maps localization, Appl Sci, 10, 4716, 10.3390/app10144716 Saeedi, 2019, Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition, Diabetes Res Clin Pract, 157, 107843, 10.1016/j.diabres.2019.107843 Tsuji, 2020, Classification of optical coherence tomography images using a capsule network, BMC Ophthalmol, 20, 114, 10.1186/s12886-020-01382-4 Tasnim N, Hasan M, Islam I. Comparisonal study of Deep Learning approaches on Retinal OCT Image 2019:23–4. Bhende, 2018, Optical coherence tomography: A guide to interpretation of common macular diseases, Indian J Ophthalmol, 66, 20, 10.4103/ijo.IJO_902_17 Feijóo, 2020, Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy, Telecomm Policy, 44, 10.1016/j.telpol.2020.101988 Davenport, 2020, How artificial intelligence will change the future of marketing, J Acad Mark Sci, 48, 24, 10.1007/s11747-019-00696-0 Elsharkawy, 2022, A novel computer-aided diagnostic system for early detection of diabetic retinopathy using 3D-OCT higher-order spatial appearance model, Diagnostics, 12, 461, 10.3390/diagnostics12020461 Han, 2022, Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images, Sci Rep, 12, 2232, 10.1038/s41598-022-05903-7 Pin, 2022, Comparative study of transfer learning models for retinal disease diagnosis from fundus images, Comput Mater Contin, 70, 5821 Motozawa, 2019, Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes, Ophthalmol Ther, 8, 527, 10.1007/s40123-019-00207-y Yoo, 2021, Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification, Med Biol Eng Comput, 59, 401, 10.1007/s11517-021-02321-1 Yoo, 2019, The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment, Med Biol Eng Comput, 57, 677, 10.1007/s11517-018-1915-z De Fauw, 2018, Clinically applicable deep learning for diagnosis and referral in retinal disease, Nat Med, 24, 1342, 10.1038/s41591-018-0107-6 Wang, 2019, Deep learning for quality assessment of retinal OCT images, Biomed Opt Express, 10, 6057, 10.1364/BOE.10.006057 Lin, 2021, Assessing the clinical utility of expanded macular OCTs using machine learning, Transl Vis Sci Technol, 10, 32, 10.1167/tvst.10.6.32 Li, 2019, Deep learning-based automated detection of retinal diseases using optical coherence tomography images, Biomed Opt Express, 10, 6204, 10.1364/BOE.10.006204 A p, 2021, OctNET: A lightweight CNN for retinal disease classification from optical coherence tomography images, Comput Methods Programs Biomed, 200, 105877, 10.1016/j.cmpb.2020.105877 Mittal, 2021, Retinal disease classification using convolutional neural networks algorithm, Turk J Comput Math Educ, 12, 5681 Mooney P. Retinal OCT Images (optical coherence tomography). Kaggle 2018. https://www.kaggle.com/paultimothymooney/kermany2018 (accessed June 10, 2021). Srinivasan, 2014, Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images, Biomed Opt Express, 5, 3568, 10.1364/BOE.5.003568 Rasti, 2018, Macular OCT classification using a multi-scale convolutional neural network ensemble, IEEE Trans Med Imaging, 37, 1024, 10.1109/TMI.2017.2780115 Tsiakmaki, 2020, Transfer learning from deep neural networks for predicting student performance, Appl Sci, 10, 2145, 10.3390/app10062145 Toğaçar, 2019, Biyomedikal Görüntülerde Derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması, Fırat Üniversitesi Mühendislik Bilim Derg, 31, 109 Lundervold, 2019, An overview of deep learning in medical imaging focusing on MRI, Z Med Phys, 29, 102, 10.1016/j.zemedi.2018.11.002 Alshazly, 2019, Ensembles of deep learning models and transfer learning for ear recognition, Sensors (Basel), 19, 4139, 10.3390/s19194139 Baykal, 2020, Transfer learning with pre-trained deep convolutional neural networks for serous cell classification, Multimed Tools Appl, 79, 15593, 10.1007/s11042-019-07821-9 Diker A, Comert Z, Avci E, Togacar M, Ergen B. A novel application based on spectrogram and convolutional neural network for ECG classification. In: 1st Int. Informatics Softw. Eng. Conf., IEEE; 2019, p. 1–6. doi:10.1109/UBMYK48245.2019.8965506. Shao S, Li Z, Zhang T, Peng C, Yu G, Zhang X, et al. Objects365: A large-scale, high-quality dataset for object detection. In: 2019 IEEE/CVF Int. Conf. Comput. Vis., 2019, p. 8429–38. doi:10.1109/iccv.2019.00852. Wang, 2018, Ship classification in high-resolution SAR images using deep learning of small datasets, Sensors (Basel), 18, 2929, 10.3390/s18092929 Pretrained Deep Neural Networks - MATLAB & Simulink. MathWorks 2021. https://www.mathworks.com/help/deeplearning/ug/pretrained-convolutional-neural-networks.html (accessed June 11, 2021). Yamashita, 2018, Convolutional neural networks: an overview and application in radiology, Insights Imaging, 9, 611, 10.1007/s13244-018-0639-9 dos Santos, 2020, Does removing pooling layers from convolutional neural networks improve results?, SN Comput Sci, 1, 275, 10.1007/s42979-020-00295-9 Rachapudi, 2021, Improved convolutional neural network based histopathological image classification, Evol Intell, 14, 1337, 10.1007/s12065-020-00367-y Wang M, Lu S, Zhu D, Lin J, Wang Z. A high-speed and low-complexity architecture for softmax function in deep learning. In: IEEE Asia Pacific Conf. Circuits Syst., 2018, p. 223–6. doi:10.1109/apccas.2018.8605654. Kandel, 2020, The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset, ICT Express, 6, 312, 10.1016/j.icte.2020.04.010 Li, 2020, Slime mould algorithm: A new method for stochastic optimization, Futur Gener Comput Syst, 111, 300, 10.1016/j.future.2020.03.055 Dhawale, 2021, An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm, Eng Comput Ewees, 2021, Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model, Eng Comput, 10.1007/s00366-021-01342-6 Nguyen, 2020, An improved slime mold algorithm and its application for optimal operation of cascade hydropower stations, IEEE Access, 8, 226754, 10.1109/ACCESS.2020.3045975 Heidari AA. Slime mould algorithm a new method for stochastic optimization. GitHub 2020. https://github.com/aliasghar68/Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization- (accessed June 10, 2021). Toğaçar, 2019, Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti, Fırat Üniversitesi Mühendislik Bilim Derg, 31, 223 Rahmad, 2020, Performance comparison of anti-spam technology using confusion matrix classification, IOP Conf Ser Mater Sci Eng, 879, 12076, 10.1088/1757-899X/879/1/012076 Chicco, 2020, The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics, 21, 6, 10.1186/s12864-019-6413-7 Alsaggaf, 2020, Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals, Appl Acoust, 167, 10.1016/j.apacoust.2020.107429 Toğaçar, 2021, Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks, Med Biol Eng Comput, 59, 57, 10.1007/s11517-020-02290-x Lawrence, 2019, IoTNet: an efficient and accurate convolutional neural network for IoT devices, Sensors (Basel), 19, 5541, 10.3390/s19245541 Xu, 2018, Bayesian Naïve Bayes classifiers to text classification, J Inf Sci, 44, 48, 10.1177/0165551516677946 Li, 2020, Bagged tree based frame-wise beforehand prediction approach for HEVC intra-coding unit partitioning, Electron, 9, 1523, 10.3390/electronics9091523 Gul, 2020, Boosted trees algorithm as reliable spectrum sensing scheme in the presence of malicious users, Electron, 9, 1038, 10.3390/electronics9061038 Mounce, 2017, Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems, Water Resour Manag, 31, 1575, 10.1007/s11269-017-1595-8 Ashour, 2018, Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images, Heal Inf Sci Syst, 6, 21, 10.1007/s13755-018-0059-8 Adem, 2020, Diagnosis of breast cancer with stacked autoencoder and subspace kNN, Phys A Stat Mech Its Appl, 551 Peralta, 2019, Mixture of experts with entropic regularization for data classification, Entropy, 21, 190, 10.3390/e21020190 Shibui Y. Mixture of experts source code. Github 2021. https://github.com/shibuiwilliam/mixture_of_experts_keras/blob/master/MoE_MNIST2.ipynb (accessed September 20, 2021). Choi, 2017, Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database, PLoS ONE, 12, 10.1371/journal.pone.0187336 Mou, 2022, A multi-scale anomaly detection framework for retinal OCT images based on the Bayesian neural network, Biomed Signal Process Control, 75, 10.1016/j.bspc.2022.103619 Tasnim, 2019, Comparisonal study of deep learning approaches on retinal OCT image, Int Conf Innov Eng Technol, 23 He, 2022, Automatic detection of age-related macular degeneration based on deep learning and local outlier factor algorithm, Diagnostics, 12, 532, 10.3390/diagnostics12020532 Sun, 2017, Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning, J Biomed Opt, 22, 10.1117/1.JBO.22.1.016012 Thomas, 2021, A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images, Comput Methods Programs Biomed, 209, 10.1016/j.cmpb.2021.106294