Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. population health analysis, risk adjustment analytics, analysis of the eectiveness of CDS, digital image analysis applied to quality . A feature is defined as an interesting part of an image and is used as a starting point for computer vision algorithms [61]. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. In general, labeled images (training dataset) are used to perform the machine learning of the class (group) description which in turn is used for unknown (unlabeled) images [60]. Furthermore, in the previously cited works in this section, the authors did not show what is the impact of big data in their works, if any. Authors Christian Tchito Tchapga 1 , Thomas Attia Mih 1 , Aurelle Tchagna Kouanou 1 2 , Theophile Fozin Fonzin 2 3 , Platini Kuetche Fogang 4 , Brice Anicet Mezatio 2 , Daniel Tchiotsop 5 Affiliations The first part of Figure 1 is the training phase. eCollection 2022. 6, pp. Interdisciplinary sleep medicine center, Charite - Universittsmedizin Berlin. Biomedical Image Processing Projects will pave new paths and bring fresh resources for you. When category membership is known, the classification is done on the basis of a training set of data containing observations. 2021 Jul;9(13):1073. doi: 10.21037/atm-20-7436. A. Aldweesh, A. Derhab, and Z. Zheng C, Koh V, Bian F, Li L, Xie X, Wang Z, Yang J, Chew PTK, Zhang M. Ann Transl Med. 7 Biomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia. Keywords: Lung Ultrasonography (LUS), Deep Learning (DL), Frame format. 110, 2016. in [37] proposed a customized CNN network for lung image patch classification and designed a fully automatic neural-based machine learning framework to extract discriminative features from training samples and perform classification at the same time. For modality classification, we used the subfigures from the ImageCLEF 2013 and 2016 sub-figure classification task. According to the volume of the dataset, you can choose more or less than three slaves. Educational: As an interdisciplinary research area, biomedical image analysis is difficult to hand on for researchers from other communities, as it requires background knowledge from computer vision, machine learning, biomedical imaging, and clinical science. eCollection 2021. Concerning classification, they gave examples of disease classification tasks by using CNN. In: Machine learning in medical imaging MLMI 2016. 2016;49(1):136. Chiefly, you can claim and hold the best package from our team. The master manages and distributes the job to the slave. H. T. Nguyen and L. T. Nguyen, Fingerprints classification through image analysis and machine learning method, Algorithms, vol. In the Spark framework, the main program (driver) controls multiple slaves (workers) and collects results from them, whereas slaves nodes read data partitions (blocks) from a distributed file system execute some computations and save the result to disk. If nothing happens, download GitHub Desktop and try again. 29, pp. Comput Biol Med 96:128140, Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, Snchez CI et al. in 2017 made an investigation on ML algorithms for healthcare [32]. sharing sensitive information, make sure youre on a federal J Ambient Intell Humaniz Comput. Covering primary data modalities in biomedical When category membership is known, the classification is done on the basis of a training set of data containing observations. LXVIV, no. In this paper, we have focused on the concept of big data for biomedical image classification tasks and, in particular, on exploring machine learning algorithms (SVM and DL) for biomedical classification following the Spark programming model. X. Zhang, Y. Yang, and L. Shen, Spark-SIFT: a spark-based large-scale image feature extract system, in Proceedings of the 13th International Conference on Semantics, Knowledge and Grids, pp. Spark framework is able to make data suitable for iteration, query it repeatedly, and load it into memory. Part of Springer Nature. Bethesda, MD 20894, Web Policies 343361, 2020. General, DL architecture is composed of one or more convolutional layers with many hidden networks, one or more max pooling operations, and a full connection layer. 7388, 2013. 2015, Article ID 370194, 16 pages, 2015. 1732, 2018. -, Istephan S., Siadat M.-R. Unstructured medical image query using big data - an epilepsy case study. It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. -, Yang A., Troup M., Ho J. W. K. Scalability and validation of big data bioinformatics software. The authors acknowledge and thank Dr. Romanic Kengne and the InchTechs team (http://www.inchtechs.com), for their support and assistance during the conception of this work. "EAGER: Towards a multimodal smart textile medical monitoring system for Neonatal ICUs . provided a review of the studies of applying DL to neuroimaging data to investigate neurological disorders and psychiatric. In: 2018 IEEE International symposium on circuits and systems (ISCAS). 74, pp. Their proposed technique classifies normal and different classes of abnormal images, and they used fuzzy logic to assign weights to different feature values based on its discrimination capability. Classification system workflow for training and testing processes. Keywords Topics will include feature extraction and classification, pattern recognition, supervised and unsupervised learning . For detecting 19 cephalometric landmarks in dental X-ray . Li et al. U-Net: Convolutional Networks for Biomedical Image SegmentationRonneberger et al, 15 Roll up everybody! Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. AI Mag 18(4):7136, Duneja A, Puyalnithi T, Vankadara MV, Chilamkurti N (2018) Analysis of inter-concept dependencies in disease diagnostic cognitive maps using recurrent neural network and genetic algorithms in time series clinical data for targeted treatment. I. Rizwan I Haque and J. Neubert, Deep learning approaches to biomedical image segmentation, Informatics in Medicine Unlocked, vol. Several imaging techniques have been developed, providing many approaches to the study of the human body. About. M. A. Ferrag, L. Maglaras, and J. H. Moschoyiannis, Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study Image classification, Journal of Information Security and Applications, vol. The goal is to answer "is there a cat in this image?", by predicting either yes or no. government site. 4352, 2020. Many investigations have been performed by researchers to improve classification for biomedical images [6, 7, 3136]. In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. Europe PMC. 2018;30(4):431448. Biomedical image modality classification is the problem of labelling biomedical images with their modality or in a larger sense the image type of the figure. So, to apply DL, the dataset of the image has to contain many images. Disclaimer, National Library of Medicine -, Oussous A., Benjelloun F.-Z., Ait Lahcen A., Belfkih S. Big Data technologies: a survey. Please BMC Bioinformatics. Biomedical Informatics Insights. Download Citation | On Oct 21, 2022, Xiaojie Li and others published Continual Learning of Medical Image Classification Based on Feature Replay | Find, read and cite all the research you need on . The process of classification is a function that is started when new unlabeled data comes to the system. GitHub - jtrells/biomedical-image-classification Contribute to jtrells/biomedical-image-classification development by creating an account on GitHub. 2018, Article ID 4059018, 10 pages, 2018. The number of slaves leads to the gaining of processing time. 53, pp. They reported 96.56% accuracy. https://doi.org/10.1007/s12652-018-1116-5, Esteva A, Kuprel b, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Section 2 reviews published methods in the field. 2022 Nov 19;9 (11):715. . C. Zhang, P. Yue, D. Tapete, B. Shangguan, M. Wang, and Z. Wu, A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images, International Journal of Applied Earth Observation and Geoinformation, vol. 83-84, 2019. 11, pp. 2021 Mar;88:101852. doi: 10.1016/j.compmedimag.2020.101852. M. Torrisi, G. Pollastri, and Q. There will be an infinite number of hyperplanes and SVM will select the hyperplane with maximum margin. looked for how to accelerate the processes of learning time with large-scale multilabel image classification using the CNN method for learning and building the classifier with an unknown novel group that came in a stream during the training stage [39]. The testing phase has four main steps: unlabeled biomedical image capturing, feature extraction, classifier model, and prediction. S. Istephan and M.-R. Siadat, Unstructured medical image query using big data - an epilepsy case study, Journal of Biomedical Informatics, vol. J. Luo, M. Wu, D. Gopukumar, and Y. Zhao, Big data application in biomedical research and health care: a literature review, Biomedical Informatics Insights, vol. proposed a complete big data workflow for biomedical image analysis [7]; Belle et al. In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In: Computer vision and pattern recognition 2016. This site needs JavaScript to work properly. 8600 Rockville Pike HHS Vulnerability Disclosure, Help Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches Comput Methods Programs Biomed. Z. Chen, X. J. Wu, and J. Kittler, Low-rank discriminative least squares regression for image classification, Signal Processing, vol. S. A. Lashari and R. Ibrahim, A framework for medical images classification using soft set, Procedia Technology, vol. Indeed, several valuable resources on the Internet provide techniques and functions for classification, localization, detection, and segmentation using deep learning. J. Archenaa and E. A. M. Anita, A survey of big data analytics in healthcare and government, Procedia Computer Science, vol. With capabilities like in-memory data storage and near real-time processing, the performance can be several times faster than other big data technologies. Benjelloun, A. Ait Lahcen, and S. Belfkih, Big Data technologies: a survey, Journal of King Saud University - Computer and Information Sciences, vol. X. Wu, D. Sahoo, C. Steven, and H. Hoi, Recent advances in deep learning for object detection, Neurocomputing, vol. Bioinformatics 17(12):12131223, Article The training phase in classification concerns the phase where you present your data from the training dataset (labeled biomedical images in this case), extract features, and train your model, by mapping the input with the expected output. However, as CNN is an end to end solution for image classification, it will learn the feature by itself. Biomedical image classification based on a feature concatenation and ensemble of deep CNNs. Use Git or checkout with SVN using the web URL. J Healthc Eng. 59, pp. 764773, Springer-Verlag, Berlin, Germany, 2007. Based on the previously cited literature in this section, it was observed that the classifier algorithms depend on the amount of data of images in the input of the classification system. Unable to load your collection due to an error, Unable to load your delegates due to an error. 1, p. 316, 2019. Copyright 2021 Christian Tchito Tchapga et al. It covers image processing, image filtering . They reviewed the state-of-the-art image classification techniques to diagnose human body disease and covered identification of medical image classification techniques, image modalities used, the dataset, and tradeoff for each technique [31]. 29, pp. An, S. Ding, S. Shi, and J. Li, Discrete space reinforcement learning algorithm based on support vector machine Classification, Pattern Recognition Letters, vol. The authors declare no conflicts of interest. https://doi.org/10.1007/s12652-018-0811-6, Ashtarian H, Mirzabeigi E, Mahmoodi E, Khezeli M (2017) Knowledge about cervical cancer and pap smear and the factors influencing the pap test screening among women. used the Rough Set Theory (RTS) to improve SVM for classifying digital mammography images [33]. J Healthc Eng. The authors declare no conflicts of interest. They are a special architecture of the Artificial Neural Networks (ANN) which was proposed in 1998 by Yann LeCunn. This is also used in non-local neural networks for video classification. Image Classification helps us to classify what is contained in an image. The goal is to identify "where is the cat in this image?", by drawing a bounding box around the object of interest. H. Fujiyoshi, T. Hirakawa, and T. Yamashita, Deep learning-based image recognition for autonomous driving, IATSS Research, vol. Epub 2018 Jun 26. -. Healthcare is generally delivered by health professionals. 189, 2019. 548556, 2013. In particular, machine learning and deep learning algorithms (e.g., support vector machine, neural network, and convolutional neural network) have achieved impressive results in biomedical image classification [1423]. It covers image processing, image filtering . 4348, 2020. M. A. Amanullah, R. A. ( Image credit: IVD-Net ) Benchmarks Add a Result These leaderboards are used to track progress in Medical Image Segmentation Show all 36 benchmarks Libraries Use these libraries to find Medical Image Segmentation models and implementations Clipboard, Search History, and several other advanced features are temporarily unavailable. Deep transfer learning approaches for bleeding detection in endoscopy images. MeSH 115, 2017. Another instance is if the size of the image considered is too different from one another, it will cause an unbalanced loading in the Spark. J Healthc Eng. Hence, each specialist will be limited to visualizing only the biomedical images essentially related to his field of competence, which is somewhat restrictive. Y. Fang, J. Zhao, L. Hu, X. Ying, Y. Pan, and X. Wang, Image classification toward breast cancer using deeply-learned quality features, Journal of Visual Communication and Image Representation, vol. For instance, Figure 3 shows the links between four nodes to perform data processing. Q. Li, W. Cai, Z. Wang, Y. Zhou, D. G. Feng, and M. Chen, Medical image classification with convolutional neural network, in Proceedings of the 13th International Conference on Control, Automation, Robotics & Vision Marina Bay Sands (ICARCV), pp. Epub 2021 Jul 14. 86101, 2019. Our method has been thoroughly tested both with small datasets and partially annotated biomedical datasets; and, it outperforms, both in terms of speed and accuracy, the existing AutoML tools when working with small datasets; and, might improve the accuracy of models up to a 10% when working with partially annotated datasets. Please enable it to take advantage of the complete set of features! The goal of this paper is to perform a survey of classification algorithms for biomedical images. Literature-based image informatics techniques are essential for managing the rapidly increasing volume of information in the biomedical domain. A. Tchagna Kouanou, D. Tchiotsop, T. Fozin Fonzin, M. Bayangmbe, and R. Tchinda, Real-time image compression system using an embedded board, Science Journal of Circuits, Systems and Signal Processing, vol. Comput Med Imaging Graph. 11531162, 2012. Nalepa and Kawulok in 2019 performed an extensive survey on existing techniques and methods to select SVM training data from large datasets and concluded that the DL will be more efficient than SVM for large datasets [41]. For access to the dataset, please contact the ImageCLEFmed: The Medical Task 2016 organizers. 15, pp. However, the CNN exploits spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers [72]. Our AutoML method combines transfer learning with a new semi-supervised learning procedure to train models when few annotated images are available. When the classifier model has been derived, any unlabeled biomedical images can be presented to the model in order to make predictions about the group to which such images belong. 2021 . in [7], the authors present a workflow performing the steps of acquisition of biomedical image data, analysis, storage, processing, querying, classification, and automatic diagnosis of biomedical images. P. Hhnel, J. Mare, J. Monteil, and A. ODonnch, Using deep learning to extend the range of air pollution monitoring and forecasting, Journal of Computational Physics, vol. X. Zhu, Z. Li, X. Li, S. Li, and F. Dai, Attention aware perceptual enhancement nets for low-resolution image classification, Information Sciences, vol. ImageCLEFmed: The Medical Task 2016 organizers. A multi-class support vector machine is then used to classify a new image depicting coronary arteries. 1, pp. 50, Article ID 102419, 2020. and measure a physical property of the human body (e.g. In: 2005 Nature inspired smart information systems (NiSIS), Albufeira, Portugal, Jeon G (2017) Computational intelligence approach for medical images by suppressing noise. D. Jaswal, V. Sowmya, and K. P. Soman, Image classification using convolutional neural networks, International Journal of Advancements in Research & Technology, vol. These drawbacks prevent the adoption of these techniques outside the machine-learning community. Accessibility Job execution Apache Spark in four clusters: one master and three slaves. R. Yan, F. Ren, Z. Wang et al., Breast cancer histopathological image classification using a hybrid deep neural network, Methods, vol. https://orcid.org. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Y. Jiang, Z. Li, L. Zhang, and P. Sun, An improved SVM classifier for medical image classification, in RSEISP, et al., Ed., pp. We can also conclude that, nowadays, as the size of the training data set grows, ML algorithms become more effective. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 171, 2020. View Stony Brook Dept of Biomedical Informatics education.pdf from BMI 512 at Oregon Health & Science University. J. H. Thrall, X. Li, Q. Li et al., Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success, American College of Radiology, Reston, VA, USA, 2017. Based on the Spark framework, this work proposes an algorithm to perform the steps of the proposed classification workflow. Careers. J Ambient Intell Humaniz Comput 111, Ju C, Bibaut A, van der Laan M (2018) The relative performance of ensemble methods with deep convolutional neural networks for image classification. 100, 2019. The work presented in this paper allows the use of deep learning techniques to solve an image classification problem with few resources. 5875, 2017. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In: Proceedings of the 7th international conference on neural information processing systems. In addition to Map and Reduce operations, Spark also supports SQL queries, streaming data, machine learning, and graph processing data [7]. A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. The number of them can be different depending on the image, so we add some clauses to make our feature vector always have the same size. We wish to acknowledge the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on Campus (URECA) program. Our data with the Creative Commons (CC) License is easy to use for educational purposes. 105, pp. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. L. Wang, Ed.in Support Vector Machines: Theory and Applications, p. 503, Springer, Berlin, Germany, 2005. Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. Med Image Anal 42:6088, Liu D, Wang S, Huang D, Deng G, Zeng F, Chen H (2016) Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. eCollection 2022. (WHO) It includes : Syringes, needles, ampules, Organs and body parts Dressings, disposable plastics Microbiological waste The impact of deep learning, Zeitschrift fr Medizinische Physik, vol. (2017) A survey on deep learning in medical image analysis. On the other hand, ML is the ability to learn and improve automatically from experience without explicit programming [7]. sharing sensitive information, make sure youre on a federal (2019)Cite this article. A conclusion and future work are provided in Section 5. Clipboard, Search History, and several other advanced features are temporarily unavailable. 2020 Apr;104:101822. doi: 10.1016/j.artmed.2020.101822. See this image and copyright information in PMC. The training phase has traditionally three main steps: labeled biomedical image dataset retrieval, feature extraction, and machine learning algorithm (SVM or CNN). Big data application in biomedical research and health care: a literature review. Biomedical image classification made easier thanks to transfer and semi-supervised learning - ScienceDirect Computer Methods and Programs in Biomedicine Volume 198, January 2021, 105782 Biomedical image classification made easier thanks to transfer and semi-supervised learning A.Ins C.Domnguez J.Heras E.Mata V.Pascual DL techniques are conquering the prevailing traditional approaches of the neural network; when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities [22, 66, 67]. A. 102127, 2019. 12, Article ID e115892, 2014. Use the create_microscopy_multilabel_folds.py to create the folds for k-fold cross validation. Vieira et al. eCollection 2021. 2143, 2018. It was noticed that none of these works have made their classification with big data tools. The goal of this paper is to perform a survey of classification algorithms for biomedical images. Convolutional neural network (CNN) architecture. 1938, 2019. 2, no. HHS Vulnerability Disclosure, Help 3, pp. Therefore, keeping such early stages intact 136, 2020. C. S. Lo and C. M. Wang, Support vecto machine for breast MR image classification, Computers & Mathematics with Applications, vol. A tag already exists with the provided branch name. 4, pp. P. M. Ferreira, M. A. T. Figueiredo, and P. M. Q. Aguiar, Content-based image classification: a non-parametric approach, 2018. 103, 2020. Medical image processing requires a comprehensive environment for data access, analysis, processing, visualization, and algorithm development. In 2020, Zhang et al. 3853, 2018. L. C. C. Bergamasco and L. S. Nunes Fatima, Intelligent retrieval and classification in three-dimensional biomedical imagesa systematic mapping, Computer Science Review, vol. An image is represented by a set of descriptors that structure the feature vectors and is formed by pixels, which may or may not represent features. Author: Kenji Suzuki Publisher: Springer Science & Business Media ISBN: 1461472458 Category : Technology & Engineering Languages : en Pages : 406 View. J. Kim, J. Hong, and H. Park, Prospects of deep learning for medical imaging, Precision and Future Medicine, vol. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Our data with the Creative Commons (CC) License is easy to use for educational purposes. Before In addition, we have proven that our AutoML method outperforms other AutoML tools both in terms of accuracy and speed when working with small datasets. 2022 Jun 8;23(1):223. doi: 10.1186/s12859-022-04764-1. 13, no. Algorithm 2 predicts the images class by identifying to which set of categories this image belongs. 12091215, 2015. It uses technologies like imaging, radiology, endoscopy, photograph, tomography, etc. They also provided an insight into the deep features that have been learned through training, which will help in analyzing various abstraction of features ranging from low level to high level and their role in the final classification, and obtained a test accuracy of 81% [36]. As shown in the workflow, the classification process is performed in two basic steps. Di Ciaccio and G. M. Giorgi, Deep learning for supervised classification, Rivista Italiana di Economia Demografia e Statistica, vol. 24, no. SVM and DL are then used, respectively, in this regard. Every mathematical or positional property of biomedical images is useful for . V. F. Murilo, M. V. F. Menezes, L. C. B. Torres, and A. P. Braga, Width optimization of RBF kernels for binary classification of support vector machines: a density estimation-based approach, Pattern Recognition Letters, vol. Non-existent images cannot be classified by computer The image must have the property of reality, in the sense of not existing only in fiction. The network has a series of subsampling and convolutional layers. 2019;7:11093-11104. doi: 10.1109/ACCESS.2019.2891970. IEEE Trans Med Imaging 32(10):18781889, He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Join Karol Zak for a review of this seminal paper on . To perform this comparison, we are based on some works done in the literature. 8600 Rockville Pike Where we evaluate different deep learning approaches for biomedical image classification. 16, pp. Tchagna Kouanou A, Mih Attia T, Feudjio C, Djeumo AF, Ngo Mouelas A, Nzogang MP, Tchito Tchapga C, Tchiotsop D. J Healthc Eng. In the first step, a classifier model is built based on the labeled biomedical image using ML (SVM or CNN) algorithms. 20, pp. 7, no. 62, pp. 431448, 2018. A good classification performed essentially leads to a good automatic diagnosis of diseases on an image. However, the performances of the Spark framework can be decreased in some situations: especially during the feature extraction, in a situation where there are some small images in the dataset (unlabeled biomedical images/labeled biomedical images). Nowadays, many works are performed to use big data to manage and analyze healthcare systems. Co-PI. Many researches as Wang et al., Tchagna Kouanou et al., or Chowdharya et al. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Marshall, S.; Ren, J.; Tschannerl, J.; Kao, F. The properties of the cornea based on hyperspectral imaging: Optical biomedical engineering perspective. In Deep Learning, Convolutional Neural Networks are a class of Deep Neural Networks that are mostly used in visual imagery. In addition, using deep learning libraries and tuning the hyperparameters of the networks trained with them might be challenging for several users. In the next section, an algorithm to perform some stages of Figure 1 is presented, based on the Spark framework of image classification in [7]. Educational: As an interdisciplinary research area, biomedical image analysis is difficult to hand on for researchers from other communities, as it requires background knowledge from computer vision, machine learning, biomedical imaging, and clinical science. 1075-1083. In 2018, the USA generated a zettabyte of healthcare data [1]. DL particularly CNN has shown an intrinsic ability to automatically extract the high-level representations from big data [36]. J Ambient Intell Humaniz Comput. For example it is fine if the image exists only in trinary (base 3 representation, three-level representation) provided that you are using a computer that can read trinary. surveyed medical image classification techniques. Copyright 2020 Elsevier B.V. All rights reserved. The healthcare field is distinctively different from other fields. Epub 2022 Mar 3. Le, Deep learning methods in protein structure prediction, Computational and Structural Biotechnology Journal, vol. 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