A Survey of Medical Image Analysis Based on Machine Learning Techniques
DOI:
https://doi.org/10.29304/jqcm.2023.15.1.1139Keywords:
Medical image, machine learning, deep learning, neural networksAbstract
Machine learning is a result of the availability and accessibility of a massive amount of data collected via sensors and the internet. The concept of machine learning demonstrates and spreads the fact that computers can improve themselves. Deep learning is causing a paradigm shift in medical image analysis. A medical image is a visual representation of the interior of a body, typically used for diagnostic or therapeutic purposes. Researchers and policymakers interested in healthcare outcomes should read this; this research provides an overview of machine learning at a high level. Computer vision is the field of using computer algorithms to understand and analyze visual data, and machine learning is a key tool for developing these algorithms. This review discovered that there are three varieties of machine learning strategies: supervised, unsupervised, and semi-supervised, and they seem to be gaining traction in risk assessment, disease prognosis, and image-based diagnosis, with increasing success. Convolutional neural networks (CNNs), k-means clustering, random forests, transductive learning, and support vector machines are among the most commonly used algorithms. Image analysis using CNN is the most effective method for medical imaging.
Downloads
Download data is not yet available.
References
[1] Patrick Doupe, James Faghmous, Sanjay Basu “Machine Learning for Health Services Researchers “,
Value Health , (2019), 22 .
[2] Mohamed Alloghani , Ahmed Aljaaf , Abir Hussain, Thar Baker, Jamila Mustafina, Dhiya Al-Jumeily and Mohammed Khalaf “Implementation of machine learning algorithms to create diabetic patient re-admission profiles “BMC Medical Informatics and Decision Making ,(2019) , 19 .
[3] Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, And Srujana Rao Yarasi “Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis “METALLURGICAL AND MATERIALS TRANSACTIONS A ,(2020), 51A .
[4] Asharul Islam Khan , Salim Al-Habsi “Machine Learning in Computer Vision “ Procedia Computer Science ,( 2020),167.
[5] Ranjay Krishna “computer vision : foundations and applications” Stanford University , (2017)
[6] Richard Szeliski “Computer Vision: Algorithms and Applications” Springer Nature Switzerland AG,(2022)
[7] Justin Ker, Lipo Wang , Jai Rao, And Tchoyoson Lim " Deep Learning Applications In
Medical Image Analysis “ , IEEE. Translations , (2018),6
[8] Chitra T. Wasnik, Kanchan Mankar “Machine Learning Techniques: A Review “,
International Journal for Research in Applied Science & Engineering Technology (IJRASET) ,
,(2019),7
[9] Batta Mahesh “Machine Learning Algorithms -A Review” International Journal of Science and Research (IJSR) , (2020) ,9
[10] Daisuke Komura , Shumpei Ishikawa “Machine Learning Methods for Histopathological Image Analysis “Computational and Structural Biotechnology Journal , (2018),16.
[11] Saad Shafiq, Atif Mashkoor, Christoph, Mayr-Dorn And Alexander, Egyed” A Literature Review of Machine Learning and Software Development Life cycle Stages” IEEE Access ,(2021) ,9.
[12] Geert Litjens , Thijs Kooi , Babak Ehteshami Bejnordi , Arnaud Arindra Adiyoso Setio , Francesco Ciompi, Mohsen Ghafoorian, JeroenA.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez “A survey on deep learning in medical image analysis “Medical Image Analysis , (2017) , 42.
[13] Antony, J., McGuinness, K., Connor, N.E.O., Moran, K., “Quantifying radio- graphic knee osteoarthritis severity using deep convolutional neural networks”. arxiv: 1609.02469 .(2016 ).
[14] Kim, E. , Cortre-Real, M. , Baloch, Z. ” A deep semantic mobile application for thyroid cytopathology”. In: Proceedings of the SPIE on Medical Imaging “ (2016a)
[15] Erick Moen, Dylan Bannon , Takamasa Kudo, William Graf, Markus Covert and David Van Valen “Deep learning for cellular image analysis “ Natural Methods,(2019) 16.
[16] Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B., “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation”. Med. Image Anal. (2017)
[17] Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I.W.M., de Leeuw, F.-E., Mar- chiori, E., van Ginneken, B., Platel, B., “Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation” . In: Proceedings of the IEEE International Symposium on Biomedical Imaging, (2016b).
[18] Susama Bagchi, Kim Gaik Tay, Audrey Huong, Sanjoy Kumar Debnath “Image processing and machine learning techniques used in computer-aided detection system for mammogram screening-A review “ , International Journal of Electrical and Computer Engineering (IJECE),(2020) ,10.
[19] Tara A. Retson, Alexandra H. Besser, Sean Sall, Daniel Golden and Albert Hsiao “Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging “ Thorac Imaging , 2019,34.
[20] Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L. Kline ” Machine Learning for Medical Imaging1 “ , radiographics, (2017) 37.
[21] Marleen de Bruijne “Machine learning approaches in medical image analysis: from detection to diagnosis”, Medical Image Analysis,( 2016), 33.
[22] Palechor, F. M., & de la Hoz Manotas, A.. Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Data in Brief, (2019) 104344.
[23] Singh, K.D., Tancev, G., Decrue, F. et al. Anal Bioanal Chem (2019) 411: 4883. [Web Link]
[24] Ayres de Campos et al. SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318, (2010).
[25] G. Demiroz, H. A. Govenir, and N. Ilter, "Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals", Aritificial Intelligence in Medicine
[26] Michael Kahn, MD, PhD, Washington University, St. Louis, MO
[27] Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web Link]
[28] David Gil, Jose Luis Girela, Joaquin De Juan, M. Jose Gomez-Torres, and Magnus Johnsson." Predicting seminal quality with artificial intelligence methods". Expert Systems with Applications, (2012),39.
[29] C. Sapsanis, Recognition of Basic Hand Movements Using Electromyography, Diploma Thesis, University of Patras, (2013).
[30] M. A. Islam, S. Akter, M. S. Hossen, S. A. Keya, S. A. Tisha and S. Hossain, 'Risk Factor Prediction of Chronic Kidney Disease based on Machine Learning Algorithms,' 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, (2020) .
[31] Maya Belen Stark, Automatic detection and segmentation of shoulder implants in X-ray images, MS thesis, San Francisco State University, (2018).
[32] Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, (2018), 18.
[33] Maya Belen Stark, Automatic detection and segmentation of shoulder implants in X-ray images, MS thesis, San Francisco State University, (2018)
[34] Felix Gräßer, Surya Kallumadi, Hagen Malberg, and Sebastian Zaunseder. 2018. Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health
[35] Kelwin Fernandes, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing,( 2017).
[36] Sikora, Marek, Wrobel, Lukasz, and Gudy, Adam" GuideR: A guided separate-and-conquer rule learning in classification, regression, and survival settings ", Knowledge-Based Systems, (2019), 173.
[37] Jawad Rasheed • Alaa Ali Hameed • Chawki Djeddi • Akhtar Jamil • Fadi Al Turjman “A machine learning based framework for diagnosis of COVID 19 from chest X ray images “Interdisciplinary Sciences: Computational Life Sciences,(2021) , 13
[38] L. Papp , C. P. Spielvogel , B. Grubmüller , M. Grahovac , D. Krajnc , B. Ecsedi , R. A.M. Sareshgi , D. Mohamad , M. Hamboeck, I.Rausch, M. Mitterhauser , W. Wadsak , A. R. Haug , L. Kenner , P. Mazal ,M. Susani , S. Hartenbach , P. Baltzer , T. H. Helbich , G. Kramer , S.F. Shariat , T. Beyer , M. Hartenbach , M. Hacker “Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga] Ga-PSMA-11 PET/MRI” European Journal of Nuclear Medicine and Molecular Imaging,(2021),48
[39] Asmir Vodencarevic, Koray Tascilar, Fabian Hartmann, Michaela Reiser, Axel J. Hueber, Judith Haschka, Sara Bayat, Timo Meinderink, Johannes Knitza, Larissa Mendez, Melanie Hagen, Gerhard Krönke, Jürgen Rech, Bernhard Manger, Arnd Kleyer, Marcus Zimmermann-Rittereiser, Georg Schett , David Simon and on behalf of the RETRO study group “Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs “Arthritis Research and Therapy,( 2021) ,23.
[40] Alessio Mancini, Leonardo Vito, Elisa Marcelli, Marco Piangerelli, Renato De Leone, Sandra Pucciarelli and Emanuela Merelli “Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS” “BMC Bioinformatics,(2020), 21.
[41] Tulin Ozturk , Muhammed Talo , Eylul Azra Yildirim , Ulas Baran Baloglu , Ozal Yildirim , U. Rajendra Acharya “Automated detection of COVID-19 cases using deep neural networks with X-ray images “Computers in Biology and Medicine , (2020), 121.
[42] Jason C. Cai, Zeynettin Akkus, Kenneth A. Philbrick, Arunnit Boonrod, Safa Hoodeshenas, Alexander D. Weston, Pouria Rouzrokh, Gian Marco Conte, Atefeh Zeinoddini, David C. Vogelsang, Qiao Huang, Bradley J. Erickson” Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning “Radiology: Artificial Intelligence, (2020),2.
[43] Eyal Klang, Yiftach Barash, Reuma Yehuda Margalit, Shelly Soffer, Orit Shimon, Ahmad Albshesh, Shomron Ben-Horin, Marianne Michal Amitai Rami Eliakim, Uri Kopylov “Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy “Gastrointestinal Endoscopy,( 2020), 91.
[44] Andrés García-Floriano a , Ángel Ferreira-Santiago , Oscar Camacho-Nieto , Cornelio Yáñez-Márquez “A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images” Computers and Electrical Engineering,( 2019), 75.
[45] Sergio Martínez-Agüero , Inmaculada Mora-Jiménez , Jon Lérida-García , Joaquín Álvarez-Rodríguez and Cristina Soguero-Ruiz “Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit “entropy , (2019),21.
[46] Yan A. Ivanenkov , Alex Zhavoronkov , Renat S. Yamidanov , Ilya A. Osterman , Petr V. Sergiev , Vladimir A. Aladinskiy , Anastasia V. Aladinskaya , Victor A. Terentiev , Mark S. Veselov , Andrey A. Ayginin , Victor G. Kartsev , Dmitry A. Skvortsov , Alexey V. Chemeris , Alexey Kh. Baimiev , Alina A. Sofronova , Alexander S. Malyshev , Gleb I. Filkov , Dmitry S. Bezrukov , Bogdan A. Zagribelnyy , Evgeny O. Putin , Maria M. Puchinina and Olga A. Dontsova ” Identification of Novel Antibacterials Using Machine Learning Techniques” Frontiers in Pharmacology,(2019),10.
[47] Debabrata Ghosh, Shivam Sharma, Eeshan Hasan, Shabina Ashraf, Vaibhav Singh, Dinesh Tewari, Seema Singh, Mudit Kapoor, and Debarka Sengupta “Machine learning based prediction of antibiotic sensitivity in patients with critical illness” Machine learning based prediction of antibiotic sensitivity in patients with critical illness ,( 2019)
[48] P.J. Sudharshan, Caroline Petitjean, Fabio Spanhol, Luis Oliveira, Laurent Heutte, Paul Honeine “Multiple Instance Learning for Histopathological Breast Cancer Image ClassiÞcation “Expert Systems With Applications , (2019), 117.
[49] Jen Hong Tan , Sulatha V. Bhandary , Sobha Sivaprasad , Yuki Hagiwara , Akanksha Bagchi , U. Raghavendra , A. Krishna Rao , Biju Raju , Nitin Shridhara Shetty f,Arkadiusz Gertych , Kuang Chua Chua , U. Rajendra Acharya “Age-related Macular Degeneration detection using deep convolutional neural network” Future Generation Computer Systems, (2018),87.
[50] Seok Won Chung, Seung Seog Han, Ji Whan Lee, Kyung-Soo Oh, Na Ra Kim, Jong Pil Yoon, Joon Yub Kim, Sung Hoon Moon, Jieun Kwon, Hyo-Jin Lee, Young-Min Noh & Youngjun Kim “Automated detection and classification of the proximal humerus fracture by using deep learning algorithm “Acta Orthopaedica , (2018), 89
[51] Danni Chenga, Manhua Liu, Jianliang Fu, Yaping Wang “Classification of MR Brain Images by Combination of Multi-CNNs for AD Diagnosis “Ninth International Conference on Digital Image Processing (ICDIP 2017), (2017)
[52] Benjamin Q. Huynh , Hui Li , Maryellen L. Giger “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks “Journal of Medical Imaging , (2016) , 3.
[53] Muhammad Zubair , Jinsul Kim , Changwoo Yoon “An Automated ECG Beat Classification System Using Convolutional Neural Networks “Institute of Electrical and Electronics Engineers IEEE Computer Society ,(2016)
Value Health , (2019), 22 .
[2] Mohamed Alloghani , Ahmed Aljaaf , Abir Hussain, Thar Baker, Jamila Mustafina, Dhiya Al-Jumeily and Mohammed Khalaf “Implementation of machine learning algorithms to create diabetic patient re-admission profiles “BMC Medical Informatics and Decision Making ,(2019) , 19 .
[3] Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, And Srujana Rao Yarasi “Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis “METALLURGICAL AND MATERIALS TRANSACTIONS A ,(2020), 51A .
[4] Asharul Islam Khan , Salim Al-Habsi “Machine Learning in Computer Vision “ Procedia Computer Science ,( 2020),167.
[5] Ranjay Krishna “computer vision : foundations and applications” Stanford University , (2017)
[6] Richard Szeliski “Computer Vision: Algorithms and Applications” Springer Nature Switzerland AG,(2022)
[7] Justin Ker, Lipo Wang , Jai Rao, And Tchoyoson Lim " Deep Learning Applications In
Medical Image Analysis “ , IEEE. Translations , (2018),6
[8] Chitra T. Wasnik, Kanchan Mankar “Machine Learning Techniques: A Review “,
International Journal for Research in Applied Science & Engineering Technology (IJRASET) ,
,(2019),7
[9] Batta Mahesh “Machine Learning Algorithms -A Review” International Journal of Science and Research (IJSR) , (2020) ,9
[10] Daisuke Komura , Shumpei Ishikawa “Machine Learning Methods for Histopathological Image Analysis “Computational and Structural Biotechnology Journal , (2018),16.
[11] Saad Shafiq, Atif Mashkoor, Christoph, Mayr-Dorn And Alexander, Egyed” A Literature Review of Machine Learning and Software Development Life cycle Stages” IEEE Access ,(2021) ,9.
[12] Geert Litjens , Thijs Kooi , Babak Ehteshami Bejnordi , Arnaud Arindra Adiyoso Setio , Francesco Ciompi, Mohsen Ghafoorian, JeroenA.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez “A survey on deep learning in medical image analysis “Medical Image Analysis , (2017) , 42.
[13] Antony, J., McGuinness, K., Connor, N.E.O., Moran, K., “Quantifying radio- graphic knee osteoarthritis severity using deep convolutional neural networks”. arxiv: 1609.02469 .(2016 ).
[14] Kim, E. , Cortre-Real, M. , Baloch, Z. ” A deep semantic mobile application for thyroid cytopathology”. In: Proceedings of the SPIE on Medical Imaging “ (2016a)
[15] Erick Moen, Dylan Bannon , Takamasa Kudo, William Graf, Markus Covert and David Van Valen “Deep learning for cellular image analysis “ Natural Methods,(2019) 16.
[16] Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B., “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation”. Med. Image Anal. (2017)
[17] Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I.W.M., de Leeuw, F.-E., Mar- chiori, E., van Ginneken, B., Platel, B., “Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation” . In: Proceedings of the IEEE International Symposium on Biomedical Imaging, (2016b).
[18] Susama Bagchi, Kim Gaik Tay, Audrey Huong, Sanjoy Kumar Debnath “Image processing and machine learning techniques used in computer-aided detection system for mammogram screening-A review “ , International Journal of Electrical and Computer Engineering (IJECE),(2020) ,10.
[19] Tara A. Retson, Alexandra H. Besser, Sean Sall, Daniel Golden and Albert Hsiao “Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging “ Thorac Imaging , 2019,34.
[20] Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L. Kline ” Machine Learning for Medical Imaging1 “ , radiographics, (2017) 37.
[21] Marleen de Bruijne “Machine learning approaches in medical image analysis: from detection to diagnosis”, Medical Image Analysis,( 2016), 33.
[22] Palechor, F. M., & de la Hoz Manotas, A.. Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Data in Brief, (2019) 104344.
[23] Singh, K.D., Tancev, G., Decrue, F. et al. Anal Bioanal Chem (2019) 411: 4883. [Web Link]
[24] Ayres de Campos et al. SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318, (2010).
[25] G. Demiroz, H. A. Govenir, and N. Ilter, "Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals", Aritificial Intelligence in Medicine
[26] Michael Kahn, MD, PhD, Washington University, St. Louis, MO
[27] Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web Link]
[28] David Gil, Jose Luis Girela, Joaquin De Juan, M. Jose Gomez-Torres, and Magnus Johnsson." Predicting seminal quality with artificial intelligence methods". Expert Systems with Applications, (2012),39.
[29] C. Sapsanis, Recognition of Basic Hand Movements Using Electromyography, Diploma Thesis, University of Patras, (2013).
[30] M. A. Islam, S. Akter, M. S. Hossen, S. A. Keya, S. A. Tisha and S. Hossain, 'Risk Factor Prediction of Chronic Kidney Disease based on Machine Learning Algorithms,' 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, (2020) .
[31] Maya Belen Stark, Automatic detection and segmentation of shoulder implants in X-ray images, MS thesis, San Francisco State University, (2018).
[32] Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, (2018), 18.
[33] Maya Belen Stark, Automatic detection and segmentation of shoulder implants in X-ray images, MS thesis, San Francisco State University, (2018)
[34] Felix Gräßer, Surya Kallumadi, Hagen Malberg, and Sebastian Zaunseder. 2018. Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health
[35] Kelwin Fernandes, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing,( 2017).
[36] Sikora, Marek, Wrobel, Lukasz, and Gudy, Adam" GuideR: A guided separate-and-conquer rule learning in classification, regression, and survival settings ", Knowledge-Based Systems, (2019), 173.
[37] Jawad Rasheed • Alaa Ali Hameed • Chawki Djeddi • Akhtar Jamil • Fadi Al Turjman “A machine learning based framework for diagnosis of COVID 19 from chest X ray images “Interdisciplinary Sciences: Computational Life Sciences,(2021) , 13
[38] L. Papp , C. P. Spielvogel , B. Grubmüller , M. Grahovac , D. Krajnc , B. Ecsedi , R. A.M. Sareshgi , D. Mohamad , M. Hamboeck, I.Rausch, M. Mitterhauser , W. Wadsak , A. R. Haug , L. Kenner , P. Mazal ,M. Susani , S. Hartenbach , P. Baltzer , T. H. Helbich , G. Kramer , S.F. Shariat , T. Beyer , M. Hartenbach , M. Hacker “Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga] Ga-PSMA-11 PET/MRI” European Journal of Nuclear Medicine and Molecular Imaging,(2021),48
[39] Asmir Vodencarevic, Koray Tascilar, Fabian Hartmann, Michaela Reiser, Axel J. Hueber, Judith Haschka, Sara Bayat, Timo Meinderink, Johannes Knitza, Larissa Mendez, Melanie Hagen, Gerhard Krönke, Jürgen Rech, Bernhard Manger, Arnd Kleyer, Marcus Zimmermann-Rittereiser, Georg Schett , David Simon and on behalf of the RETRO study group “Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs “Arthritis Research and Therapy,( 2021) ,23.
[40] Alessio Mancini, Leonardo Vito, Elisa Marcelli, Marco Piangerelli, Renato De Leone, Sandra Pucciarelli and Emanuela Merelli “Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS” “BMC Bioinformatics,(2020), 21.
[41] Tulin Ozturk , Muhammed Talo , Eylul Azra Yildirim , Ulas Baran Baloglu , Ozal Yildirim , U. Rajendra Acharya “Automated detection of COVID-19 cases using deep neural networks with X-ray images “Computers in Biology and Medicine , (2020), 121.
[42] Jason C. Cai, Zeynettin Akkus, Kenneth A. Philbrick, Arunnit Boonrod, Safa Hoodeshenas, Alexander D. Weston, Pouria Rouzrokh, Gian Marco Conte, Atefeh Zeinoddini, David C. Vogelsang, Qiao Huang, Bradley J. Erickson” Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning “Radiology: Artificial Intelligence, (2020),2.
[43] Eyal Klang, Yiftach Barash, Reuma Yehuda Margalit, Shelly Soffer, Orit Shimon, Ahmad Albshesh, Shomron Ben-Horin, Marianne Michal Amitai Rami Eliakim, Uri Kopylov “Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy “Gastrointestinal Endoscopy,( 2020), 91.
[44] Andrés García-Floriano a , Ángel Ferreira-Santiago , Oscar Camacho-Nieto , Cornelio Yáñez-Márquez “A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images” Computers and Electrical Engineering,( 2019), 75.
[45] Sergio Martínez-Agüero , Inmaculada Mora-Jiménez , Jon Lérida-García , Joaquín Álvarez-Rodríguez and Cristina Soguero-Ruiz “Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit “entropy , (2019),21.
[46] Yan A. Ivanenkov , Alex Zhavoronkov , Renat S. Yamidanov , Ilya A. Osterman , Petr V. Sergiev , Vladimir A. Aladinskiy , Anastasia V. Aladinskaya , Victor A. Terentiev , Mark S. Veselov , Andrey A. Ayginin , Victor G. Kartsev , Dmitry A. Skvortsov , Alexey V. Chemeris , Alexey Kh. Baimiev , Alina A. Sofronova , Alexander S. Malyshev , Gleb I. Filkov , Dmitry S. Bezrukov , Bogdan A. Zagribelnyy , Evgeny O. Putin , Maria M. Puchinina and Olga A. Dontsova ” Identification of Novel Antibacterials Using Machine Learning Techniques” Frontiers in Pharmacology,(2019),10.
[47] Debabrata Ghosh, Shivam Sharma, Eeshan Hasan, Shabina Ashraf, Vaibhav Singh, Dinesh Tewari, Seema Singh, Mudit Kapoor, and Debarka Sengupta “Machine learning based prediction of antibiotic sensitivity in patients with critical illness” Machine learning based prediction of antibiotic sensitivity in patients with critical illness ,( 2019)
[48] P.J. Sudharshan, Caroline Petitjean, Fabio Spanhol, Luis Oliveira, Laurent Heutte, Paul Honeine “Multiple Instance Learning for Histopathological Breast Cancer Image ClassiÞcation “Expert Systems With Applications , (2019), 117.
[49] Jen Hong Tan , Sulatha V. Bhandary , Sobha Sivaprasad , Yuki Hagiwara , Akanksha Bagchi , U. Raghavendra , A. Krishna Rao , Biju Raju , Nitin Shridhara Shetty f,Arkadiusz Gertych , Kuang Chua Chua , U. Rajendra Acharya “Age-related Macular Degeneration detection using deep convolutional neural network” Future Generation Computer Systems, (2018),87.
[50] Seok Won Chung, Seung Seog Han, Ji Whan Lee, Kyung-Soo Oh, Na Ra Kim, Jong Pil Yoon, Joon Yub Kim, Sung Hoon Moon, Jieun Kwon, Hyo-Jin Lee, Young-Min Noh & Youngjun Kim “Automated detection and classification of the proximal humerus fracture by using deep learning algorithm “Acta Orthopaedica , (2018), 89
[51] Danni Chenga, Manhua Liu, Jianliang Fu, Yaping Wang “Classification of MR Brain Images by Combination of Multi-CNNs for AD Diagnosis “Ninth International Conference on Digital Image Processing (ICDIP 2017), (2017)
[52] Benjamin Q. Huynh , Hui Li , Maryellen L. Giger “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks “Journal of Medical Imaging , (2016) , 3.
[53] Muhammad Zubair , Jinsul Kim , Changwoo Yoon “An Automated ECG Beat Classification System Using Convolutional Neural Networks “Institute of Electrical and Electronics Engineers IEEE Computer Society ,(2016)
Downloads
Published
2023-02-17
How to Cite
Al Gharrawi, R. J., & Al-Joda, A. A. (2023). A Survey of Medical Image Analysis Based on Machine Learning Techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(1), Comp Page 48–67. https://doi.org/10.29304/jqcm.2023.15.1.1139
Issue
Section
Computer Articles
