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Journals / Veterinary Journal of Mehmet Akif Ersoy University / 2022 / Volume: 7 Issue: 3

Analysis and Estimation of Pathological Data and Findings with Deep Learning Methods

Ahmet Anıl Şakır(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ)0000-0003-1317-8089
Ali Hakan Işık(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ)0000-0003-3561-9375
Özlem Özmen(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ)0000-0002-1835-1082
Volkan İpek(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ)0000-0001-5874-7797
Pages
175–187
Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is thought that the results obtained from the estimation and analysis will allow rapid and accurate diagnosis in animal disease diagnoses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:As in human diseases, rapid diagnosis of animal diseases is of great importance.
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Key Findings & Data:In this study, the disease types in the data set consisting of the data examined between the years 2000-2020 belonging to the Department of Pathology of the Faculty of Veterinary Medicine of Burdur Mehmet Akif Ersoy University were estimated by using the decision tree classification model and the KNN classification model.
💡
Conclusion & Impact:It is thought that the results obtained from the estimation and analysis will allow rapid and accurate diagnosis in animal disease diagnoses.
🔬Design:Modeling & Simulation
📍Scope:2000-2020 Verileri
⚙️Variables:Pathology · classification · decision tree

Abstract

As in human diseases, rapid diagnosis of animal diseases is of great importance. In order for the disease treatments to be carried out properly, the diagnosis must be of high accuracy, as well as the rapid diagnosis. In this study, the disease types in the data set consisting of the data examined between the years 2000-2020 belonging to the Department of Pathology of the Faculty of Veterinary Medicine of Burdur Mehmet Akif Ersoy University were estimated by using the decision tree classification model and the KNN classification model. Categories such as age, type, city, and gender in the data set were analyzed in graphics. For the estimation and analysis processes to give accurate results, the data set was corrected by going through some pre-processes and the missing data in the data set was completed. It is thought that the results obtained from the estimation and analysis will allow rapid and accurate diagnosis in animal disease diagnoses.

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Cite This Article

Şakır, A. A., Işık, A. H., Özmen, Ö., İpek, V. (2022). Analysis and Estimation of Pathological Data and Findings with Deep Learning Methods. Veterinary Journal of Mehmet Akif Ersoy University, Volume: 7 Issue: 3, 175–187. https://doi.org/10.24880/maeuvfd.1121112

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References (37)

  1. Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., van der Laak, J., Bui, M. M., Vemuri, V. N., Parwani, A. V., Gibbs, J., Agosto-Arroyo, E., Beck, A. H., Kozlowski, C. (2019). Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. The Journal of Pathology, 249(3), 286-294. https://doi.org/10.1002/path.5331
  2. Barisoni, L., Gimpel, C., Kain, R., Laurinavicius, A., Bueno, G., Zeng, C., Liu, Z., Schaefer, F., Kretzler, M., Holzman, L. B., Hewitt, S. M. (2017). Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology. Clinical Kidney Journal, 10(2), 176-187. https://doi.org/10.1093/ckj/sfw129
  3. Bera, K., Schalper, K.A., Rimm, D.L., Velcheti, V., Madabhushi, A. (2019). Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16, 703-715. https://doi.org/10.1038/s41571-019-0252-y
  4. Bounsaythip, C., & Rinta-Runsala, E. (2001). Overview of Data Mining for Customer Behavior Modeling, Research Report TTE1-2001-18, VTT Information Technology.
  5. Nakhleh, R. E., & Volmar, K.E. (2015). Error Reduction and Prevention in Surgical Pathology (2nd Edition), Springer.
  6. Carlton, W. W., McGavin, M. D. (1995). Thomson’s Special Veterinary Pathology, Mosby-Yearbook, Inc., Missouri.
  7. Chang, H.Y., Jung, C.K., Woo, J.I., Lee, S., Cho, J., Kim, S.W., Kwak, T., Y. (2019). Artificial intelligence in pathology. Journal of Pathology and Translational Medicine, 53(1), 1-12. https://doi.org/10.4132/jptm.2018.12.16
  8. Cheville, N.F. (1999). Introduction to Veterinary Pathology, 2nd Ed. Iowa State University Pres.
  9. Emel G., & Taşkın Ç. (2005). Veri Madenciliğinde Karar Ağaçları ve Bir Satış Analizi Uygulaması. Eskişehir Osman Gazi Üniversitesi Sosyal Bilimler Dergisi, 6(2), 221-239.
  10. Euclidian Distance. (2021). Öklid Uzaklığı. Retrieved December 23, 2021, from https://tr.wikipedia.org/wiki/Öklid_uzaklığı
  11. Jones, T.C., & R.D. Hunt. (1993). Veterinary Pathology, Lea & Febiger, Philadelphia.
  12. Kahraman, M.M. (1996). Genel Patoloji Ders Notları, Uludağ Üniversitesi Veteriner Fakültesi.
  13. McCarty, J., Minsky, M.L., Rochester, N., Shannon, C.E. (2006). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904
  14. Mean Absolute Error. (2021). Mean Absolute Error. Retrieved December 25, 2021, from https://en.wikipedia.org/wiki/Mean_absolute_error
  15. Mean Squared Error. (2021). Mean Squared Error. Retrieved December 25, 2021, from https://en.wikipedia.org/wiki/Mean_squared_error
  16. Niazi, M. K. K., Parwani, A. V., Gürcan, M. N. (2019). Digital pathology and artificial intelligence. The Lancet Oncology, 20(5), e253-e261. https://doi.org/10.1016/S1470-2045(19)30154-8
  17. Numpy-a. (2021). NumPy. Retrieved December 20, 2021, from https://numpy.org
  18. Numpy-b. (2021). NumPy. Retrieved December 20, 2021, from https://tr.wikipedia.org/wiki/NumPy
  19. Özmen, Ö. (2006). Veteriner Genel Patoloji Ders Notları, Mehmet Akif Ersoy Üniversitesi Veteriner Fakültesi.
  20. Özmen, Ö. (2016). 2000-2015 Yılları Arasında Burdur’daki Rutin Patoloji Teşhisleri. VIII. Ulusal Veteriner Patoloji Kongresi, 1-3 Eylül 2016, Samsun.
  21. Özmen, Ö. (2021). Veteriner Fakültesi Öğrencilerinin Uygulamalı Patoloji Laboratuvar Eğitimleri ile Bilgi Düzeylerinin Arttırılması ve Çağdaş Yaklaşımlar ile Mesleğe Hazırlanması, Bilimsel Araştırma Projeleri Komisyonu, Mehmet Akif Ersoy Üniversitesi Veteriner Fakültesi.
  22. Pandas-a. (2021). pandas – Python Data Analysis Library. Retrieved December 21, 2021, from https://pandas.pydata.org
  23. Pandas-b. (2021). pandas: powerful Python data analysis toolkit. Retrieved December 21, 2021, from https://github.com/pandas-dev/pandas
  24. Pandas-c. (2021). Pandas. Retrieved December 21, 2021, from https://tr.wikipedia.org/wiki/Pandas
  25. Pandas-d. (2021). pandas.get_dummies. Retrieved December 23, 2021, from https://pandas.pydata.org/docs/reference/api/ pandas.get_dummies.html
  26. Root Mean Squared Error. (2021). Root Mean Squared Error. Retrieved December 25, 2021, from https://en.wikipedia.org/wiki/Root-mean-square_deviation
  27. Scikit Learn-a. (2021). sklearn.impute.SimpleImputer. Retrieved December 22, 2021, from, https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html
  28. Scikit Learn-b. (2021). sklearn.tree.DecisionTreeClassifier. Retrieved December 23, 2021, from https://scikit-learn.org/stable/modules/ generated/sklearn.tree.DecisionTreeClassifier.html
  29. Scikit Learn-c. (2021). sklearn.model_selection.train_test_split. Retrieved December 24, 2021, from https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
  30. Scikit Learn-d. (2021). sklearn.metrics.accuracy_score. Retrieved December 26, 2021, from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
  31. Scikit Learn-e. (2021). sklearn.neighbors.KNeighborsClassifier. Retrieved December 24, 2021, from https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier
  32. Scikit Learn-f. (2021). Nearest Neighbors. Retrieved December 23, 2021, from https://scikit-learn.org/stable/modules/neighbors.html
  33. Scikit Learn-g. (2021). sklearn.metrics.mean_squared_error. Retrieved December 24, 2021, from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
  34. Scikit Learn-h. (2021). sklearn.metrics.mean_absolute_error. Retrieved December 24, 2021, from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html
  35. Slauson, D.O., Cooper, B.J. (1990). Mechanisms of Disease A Textbook of Comparative General Pathology, 2nd Ed., Williams & Wilkins.
  36. Sütcü C., & Aytekin Ç. (2018). Veri Bilimi, Paloma Yayınevi.
  37. Zorman, M., Vili, P., Kokol, P., Peterson, M., Sprogar, M., Ojstersek, M. (2001). Finding The Right Decision Tree’s Induction Strategy for a Hard Real World Problem, International Journal of Medical Informatics, 63(1-2), 109-121. https://doi.org/10.1016/S1386-5056(01)00176-9