Research Article
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Year 2023, Volume: 1 Issue: 2, 82 - 88, 31.12.2023

Abstract

Project Number

6629

References

  • [1] Chiang, T. C., Huang, Y. S.,Chen, R. T., Huang, C. S.,Chang, R. F. 2019. “Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation.” IEEE Transactions On Medical Imaging 38(1):240-249. doi: 10.1109/TMI.2018.2860257
  • [2] Hosni, M.,Abnane, I.,Idri, A., Gea, J. M. C., Alemán, J. L. F. 2019. “Reviewing ensemble classification methods in breast cancer.” Computer Methods and Programs in Biomedicine 177:89-112. doi:10.1016/j.cmpb.2019.05.019
  • [3] Sibbering, M. and Courtney, C. A. 2019. Management of breast cancer: basic principles, Surgery (Oxford), 37(3):157-163. doi:10.1016/j.mpsur.2019.01.004
  • [4] Reidt, J. M. 1959. “Medical Ultrasonics: Diagnostic applications of ultrasound.” Proceedings of the IRE.Ultrasound 47(11):1963-1967. doi: 10.1109/JRPROC.1959.287211
  • [5] Shi, X., Cheng, H. D., Hu, L., Ju, W., Tian, J. 2010. “Detection and classification of masses in breast ultrasound images.” Digital Signal Processing 20:824-836. doi:10.1016/j.dsp.2009.10.010
  • [6] Jesneck, J., Lo, J., Baker, J. 2007. “Breast mass lesions: Computer-aided diagnosis models with mammographic and sonographic descriptors.” Radiology 244(2):390-398.doi: 10.1148/radiol.2442060712
  • [7] Stoitsis, J., Valavanis, I., Mougiakakou, S. G., Golemati, S., Nikita, A., Nikita, K. S. 2006. “Computer aided diagnosis based on medical image processing and artificial intelligence methods.” Nuclear Instruments and Methods in Physics Research A 569:591-595. doi:10.1016/j.nima.2006.08.134
  • [8] Sampaio, W. B., Diniz, E. M., Silva, A. C., Paiva, A. C., Gattass, M. 2011. “Detection of masses in mammogram images using CNN, geostatistic functions and SVM.” Computers in Biology and Medicine 41:653-664. doi:10.1016/j.compbiomed.2011.05.017
  • [9] Chiou, H. J., Chen, C. Y., Liu, T. C., Chiou, S. Y., Wang, H. K., Chou, Y. H., Chiang, H. K. 2009. “Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging.” Computerized Medical Imaging and Graphics 33:408-413. doi:10.1016/j.compmedimag.2009.03.005
  • [10] Huang, Y. L., Wang, K. L. and Chen, D. R. 2006. “Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines.” Neural Computing and Applications 15:164-169. doi:10.1007/s00521-005-0019-5
  • [11] Amin, K. M., Shahin, A. I. and Guo, Y. A. 2016. “A Novel breast tumor classification algorithm using neutrosophic score features.” Measurement 81:210-220. doi:10.1016/j.measurement.2015.12.013
  • [12] Singh, B. K., Verma, K. and Thoke, A. 2016. “Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images.” Expert Systems with Applications 66:114-123. doi:10.1016/j.eswa.2016.09.006
  • [13] Singh, B. K., Verma, K., Thoke, A. S., Suri, J. S. 2017. “Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm.” Measurement 105: 146-157. doi:10.1016/j.measurement.2017.01.016
  • [14] Lin, C., Hou, Y., Chen, T., Chen, K. 2014. “Breast nodules computer-aided diagnostic system design using fuzzy cerebellar model neural networks.” IEEE Transactions on Fuzzy Systems 22(3):693-699.doi: 10.1109/TFUZZ.2013.2269149
  • [15] Mata, C., Oliver, A., Lalande, A., Walker, P., Marti, J. 2017. “On the use of XML in medical imaging web-based applicaions.” IRBM 38:3-12. doi:10.1016/j.irbm.2016.10.001
  • [16] Grandinetti, L. and Pisacane, O. 2011. “Web based prediction for diabetes treatment.” Future Generation Computer Systems 27:139-147. doi:10.1016/j.future.2010.08.001
  • [17] Al Mamun, K. A., Alhussein, M., Sailunaz, K., İslam, M. 2017. “Cloud based framework for parkinson’s disease diagnosis and monitoring systems for healthcare applications.” Future Generation Computer Systems 66:36-47. doi:10.1016/j.future.2015.11.010
  • [18] Markiewicz, T., Korzynska, A., Kowalski, A., Chadaj, S. Z., Murawski, P., Grala, B., Lorent, M., Wdowiak, M., Zak, J., Roszkowiak, L., Kozlowski, W., Pijanowska, D. 2016. “MIAP- web based platform for the computer alanysis of microscopic images to support the pathological diagnosis.” Biocybernetics and Biomedical Engineering 36(4):597-609. doi:10.1016/j.bbe.2016.06.006
  • [19] Remeserio, B., Barreira, N., Resua, C. G., Lira, M., Giraldez, M. J., Pimental, E. Y., Penedo, M. G. 2016. “İDEAS: a web based system for dry eye assessment.” Computer Methods and Programs in Biomedicine 130:186-197. doi:10.1016/j.cmpb.2016.02.015
  • [20] Ruiz, E. C., Saez, G. G., Rigla, M., Villaplane, M., Pons, B., Hernando, M. E. 2017. “A web based clinical decision support system for gestational diabetes: automatic diet prescription and detection of insulin needs.” International Journal of Medical Informatic 102:35-49. doi: 10.1016/j.ijmedinf.2017.02.014
  • [21] Antoniou, Z. C., Giannakopoulou, G. P., Andreadis, I. I., Nikita, K. S., Ligomenides, P. A., Spyrou, G. M. 2009. “A web-accessible mammographic image database dedicated to combined training and evaluation of radiologists and machines.” In Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, ITAB; 5-7 November 2009. Larnaca, Cyprus.
  • [22] George, Y. M., Zayed, H. H., Roushdy, M. I., Elbagoury, B. M. 2014. “Remote computer aided breast detection and diagnosis system based on cytological images.” IEEE Systems Journal 8(3):949-964.doi: 10.1109/JSYST.2013.2279415
  • [23] Silva, L. C. O., Barros, A. K. and Santana, E. E. C. 2014. “A Telediagnostic System for Automatic Detection of Lesions in Digital Mammograms.” In 5th ISSNIP-IEEE Biosignals and Biorobotics Conference Biosignals and Robotics for Better and Safer Living (BRC); 26-28 May 2014. Brazil.
  • [24] Love, S., Berg, W. A., Podilchuk, C., Aldrete, A. L. L., Mascerano, A. G., Jairaj, A., Barinow, L., Hulbert, W., Mammone, R. 2016. “Breast Cancer Triage CAD for Low Resource Countries using Low- Cost Ultrasound and Minimally-Trained Operators.” In 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT); 9-11 November 2016:220-223. Cancun, Mexico.
  • [25] Huang, Q., Huang, X., Liu, L., Lin, Y., Long, X., Li, X. 2018. “A case-oriented web-based training system for breast cancer diagnosis.” Computer Methods and Programs in Biomedicine 156:73-83. doi:10.1016/j.cmpb.2017.12.028
  • [26] Uzunhisarcikli, E. and Goreke, V. 2018. “A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system.” Sadhana 43:138.doi:10.1007/s12046-018-0915-x
  • [27] Zadeh, L. A. 1975. “Fuzzy Logic and Approximate Reasoning.” Synthese 30:407-428.
  • [28] Mamdani, E. H. 1974. “Application of fuzzy algorithms for control of simple dynamic plant.” Proceedings of the Institution of Electrical Engineers 121:1585.doi: 10.1049/piee.1974.0328
  • [29] Mendel, J. M. 2007. “Type-2 Fuzzy Sets and Systems: An Overview.” IEEE Computational Intelligence Magazine 2:20-29. doi:10.1109/MCI.2007.380672
  • [30] Castillo, O. and Melin, P. 2012. Recent Advances in Interval Type-2 Fuzzy Systems. Heidelberg New York Dordrecht London, Springer.
  • [31] Kacar, S., Bayilmis, C., Cetin, O. 2014. “A web laboratory using MATLAB Builders NE for computer image processing.” Turkish Journal of Electrical Engineering & Computer Sciences 22:166-175. doi:10.3906/elk-1203-129
  • [32] Guney, E., Eksi, Z. and Cakiroglu, M. A. 2012. “WebECG : A Novel ECG simulator based on MATLAB Web Figure.” Advances in Engineering Software 45:167-174. doi:10.1016/j.advengsoft.2011.09.005
  • [33] MathWorks, MATLAB Compiler SDK - Documentation, available at https:// www.mathworks.com/help/compiler\_sdk,lastaceferences

Meme Ultrason Görüntülerinin Analizi için Web Tabanlı CADx Uygulamasına Yeni Bir Yaklaşım

Year 2023, Volume: 1 Issue: 2, 82 - 88, 31.12.2023

Abstract

Radyolojik görüntülerin değerlendirilmesinde uzmanlara yardımcı olan bilgisayar destekli tespit/teşhis (CADe/CADx) sistemleri üzerine pek çok çalışma yapılmıştır. Bu tür sistemler hastalık tespitindeki başarı oranını artırmakta ve tespit için harcanan süreyi azaltmaktadır. Son yıllarda meme ultrasonu görüntülerinde kitle tespiti/tanısına yönelik akademik ve ticari CADx uygulamalarının bir kısmı web uygulamaları olarak geliştirilmiştir. Bu çalışmada, yeni bir sınıflandırıcı mimarisine sahip, internet bağlantısı sayesinde bir uzmanın zaman ve mekân kısıtlaması olmaksızın hastaları takip edebildiği, özgün, başarı oranı yüksek bir CADx web uygulaması geliştirilmiştir. Geliştirilen uygulama, ticari uygulamalardan farklı olarak, asistan doktorların veya tıp öğrencilerinin radyoloji alanındaki uygulamalarını artırmaya yönelik bir eğitim aracı olarak da kullanılabilir.

Supporting Institution

Erciyes Üniversitesi BAP

Project Number

6629

References

  • [1] Chiang, T. C., Huang, Y. S.,Chen, R. T., Huang, C. S.,Chang, R. F. 2019. “Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation.” IEEE Transactions On Medical Imaging 38(1):240-249. doi: 10.1109/TMI.2018.2860257
  • [2] Hosni, M.,Abnane, I.,Idri, A., Gea, J. M. C., Alemán, J. L. F. 2019. “Reviewing ensemble classification methods in breast cancer.” Computer Methods and Programs in Biomedicine 177:89-112. doi:10.1016/j.cmpb.2019.05.019
  • [3] Sibbering, M. and Courtney, C. A. 2019. Management of breast cancer: basic principles, Surgery (Oxford), 37(3):157-163. doi:10.1016/j.mpsur.2019.01.004
  • [4] Reidt, J. M. 1959. “Medical Ultrasonics: Diagnostic applications of ultrasound.” Proceedings of the IRE.Ultrasound 47(11):1963-1967. doi: 10.1109/JRPROC.1959.287211
  • [5] Shi, X., Cheng, H. D., Hu, L., Ju, W., Tian, J. 2010. “Detection and classification of masses in breast ultrasound images.” Digital Signal Processing 20:824-836. doi:10.1016/j.dsp.2009.10.010
  • [6] Jesneck, J., Lo, J., Baker, J. 2007. “Breast mass lesions: Computer-aided diagnosis models with mammographic and sonographic descriptors.” Radiology 244(2):390-398.doi: 10.1148/radiol.2442060712
  • [7] Stoitsis, J., Valavanis, I., Mougiakakou, S. G., Golemati, S., Nikita, A., Nikita, K. S. 2006. “Computer aided diagnosis based on medical image processing and artificial intelligence methods.” Nuclear Instruments and Methods in Physics Research A 569:591-595. doi:10.1016/j.nima.2006.08.134
  • [8] Sampaio, W. B., Diniz, E. M., Silva, A. C., Paiva, A. C., Gattass, M. 2011. “Detection of masses in mammogram images using CNN, geostatistic functions and SVM.” Computers in Biology and Medicine 41:653-664. doi:10.1016/j.compbiomed.2011.05.017
  • [9] Chiou, H. J., Chen, C. Y., Liu, T. C., Chiou, S. Y., Wang, H. K., Chou, Y. H., Chiang, H. K. 2009. “Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging.” Computerized Medical Imaging and Graphics 33:408-413. doi:10.1016/j.compmedimag.2009.03.005
  • [10] Huang, Y. L., Wang, K. L. and Chen, D. R. 2006. “Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines.” Neural Computing and Applications 15:164-169. doi:10.1007/s00521-005-0019-5
  • [11] Amin, K. M., Shahin, A. I. and Guo, Y. A. 2016. “A Novel breast tumor classification algorithm using neutrosophic score features.” Measurement 81:210-220. doi:10.1016/j.measurement.2015.12.013
  • [12] Singh, B. K., Verma, K. and Thoke, A. 2016. “Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images.” Expert Systems with Applications 66:114-123. doi:10.1016/j.eswa.2016.09.006
  • [13] Singh, B. K., Verma, K., Thoke, A. S., Suri, J. S. 2017. “Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm.” Measurement 105: 146-157. doi:10.1016/j.measurement.2017.01.016
  • [14] Lin, C., Hou, Y., Chen, T., Chen, K. 2014. “Breast nodules computer-aided diagnostic system design using fuzzy cerebellar model neural networks.” IEEE Transactions on Fuzzy Systems 22(3):693-699.doi: 10.1109/TFUZZ.2013.2269149
  • [15] Mata, C., Oliver, A., Lalande, A., Walker, P., Marti, J. 2017. “On the use of XML in medical imaging web-based applicaions.” IRBM 38:3-12. doi:10.1016/j.irbm.2016.10.001
  • [16] Grandinetti, L. and Pisacane, O. 2011. “Web based prediction for diabetes treatment.” Future Generation Computer Systems 27:139-147. doi:10.1016/j.future.2010.08.001
  • [17] Al Mamun, K. A., Alhussein, M., Sailunaz, K., İslam, M. 2017. “Cloud based framework for parkinson’s disease diagnosis and monitoring systems for healthcare applications.” Future Generation Computer Systems 66:36-47. doi:10.1016/j.future.2015.11.010
  • [18] Markiewicz, T., Korzynska, A., Kowalski, A., Chadaj, S. Z., Murawski, P., Grala, B., Lorent, M., Wdowiak, M., Zak, J., Roszkowiak, L., Kozlowski, W., Pijanowska, D. 2016. “MIAP- web based platform for the computer alanysis of microscopic images to support the pathological diagnosis.” Biocybernetics and Biomedical Engineering 36(4):597-609. doi:10.1016/j.bbe.2016.06.006
  • [19] Remeserio, B., Barreira, N., Resua, C. G., Lira, M., Giraldez, M. J., Pimental, E. Y., Penedo, M. G. 2016. “İDEAS: a web based system for dry eye assessment.” Computer Methods and Programs in Biomedicine 130:186-197. doi:10.1016/j.cmpb.2016.02.015
  • [20] Ruiz, E. C., Saez, G. G., Rigla, M., Villaplane, M., Pons, B., Hernando, M. E. 2017. “A web based clinical decision support system for gestational diabetes: automatic diet prescription and detection of insulin needs.” International Journal of Medical Informatic 102:35-49. doi: 10.1016/j.ijmedinf.2017.02.014
  • [21] Antoniou, Z. C., Giannakopoulou, G. P., Andreadis, I. I., Nikita, K. S., Ligomenides, P. A., Spyrou, G. M. 2009. “A web-accessible mammographic image database dedicated to combined training and evaluation of radiologists and machines.” In Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, ITAB; 5-7 November 2009. Larnaca, Cyprus.
  • [22] George, Y. M., Zayed, H. H., Roushdy, M. I., Elbagoury, B. M. 2014. “Remote computer aided breast detection and diagnosis system based on cytological images.” IEEE Systems Journal 8(3):949-964.doi: 10.1109/JSYST.2013.2279415
  • [23] Silva, L. C. O., Barros, A. K. and Santana, E. E. C. 2014. “A Telediagnostic System for Automatic Detection of Lesions in Digital Mammograms.” In 5th ISSNIP-IEEE Biosignals and Biorobotics Conference Biosignals and Robotics for Better and Safer Living (BRC); 26-28 May 2014. Brazil.
  • [24] Love, S., Berg, W. A., Podilchuk, C., Aldrete, A. L. L., Mascerano, A. G., Jairaj, A., Barinow, L., Hulbert, W., Mammone, R. 2016. “Breast Cancer Triage CAD for Low Resource Countries using Low- Cost Ultrasound and Minimally-Trained Operators.” In 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT); 9-11 November 2016:220-223. Cancun, Mexico.
  • [25] Huang, Q., Huang, X., Liu, L., Lin, Y., Long, X., Li, X. 2018. “A case-oriented web-based training system for breast cancer diagnosis.” Computer Methods and Programs in Biomedicine 156:73-83. doi:10.1016/j.cmpb.2017.12.028
  • [26] Uzunhisarcikli, E. and Goreke, V. 2018. “A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system.” Sadhana 43:138.doi:10.1007/s12046-018-0915-x
  • [27] Zadeh, L. A. 1975. “Fuzzy Logic and Approximate Reasoning.” Synthese 30:407-428.
  • [28] Mamdani, E. H. 1974. “Application of fuzzy algorithms for control of simple dynamic plant.” Proceedings of the Institution of Electrical Engineers 121:1585.doi: 10.1049/piee.1974.0328
  • [29] Mendel, J. M. 2007. “Type-2 Fuzzy Sets and Systems: An Overview.” IEEE Computational Intelligence Magazine 2:20-29. doi:10.1109/MCI.2007.380672
  • [30] Castillo, O. and Melin, P. 2012. Recent Advances in Interval Type-2 Fuzzy Systems. Heidelberg New York Dordrecht London, Springer.
  • [31] Kacar, S., Bayilmis, C., Cetin, O. 2014. “A web laboratory using MATLAB Builders NE for computer image processing.” Turkish Journal of Electrical Engineering & Computer Sciences 22:166-175. doi:10.3906/elk-1203-129
  • [32] Guney, E., Eksi, Z. and Cakiroglu, M. A. 2012. “WebECG : A Novel ECG simulator based on MATLAB Web Figure.” Advances in Engineering Software 45:167-174. doi:10.1016/j.advengsoft.2011.09.005
  • [33] MathWorks, MATLAB Compiler SDK - Documentation, available at https:// www.mathworks.com/help/compiler\_sdk,lastaceferences
There are 33 citations in total.

Details

Primary Language English
Subjects Biomedical Imaging
Journal Section Research Articles
Authors

Volkan Göreke

Project Number 6629
Publication Date December 31, 2023
Published in Issue Year 2023 Volume: 1 Issue: 2

Cite

IEEE V. Göreke, “Meme Ultrason Görüntülerinin Analizi için Web Tabanlı CADx Uygulamasına Yeni Bir Yaklaşım”, CÜMFAD, vol. 1, no. 2, pp. 82–88, 2023.