Research Article
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A Novel Histological Dataset and Machine Learning Applications

Year 2022, Volume 17, Issue 2, 185 - 196, 30.09.2022
https://doi.org/10.55525/tjst.1134354

Abstract

Histology has significant importance in the medical field and healthcare services in terms of microbiological studies. Automatic analysis of tissues and organs based on histological images is an open problem due to the shortcomings of necessary tools. Moreover, the accurate identification and analysis of tissues that is a combination of cells are essential to understanding the mechanisms of diseases and to making a diagnosis. The effective performance of machine learning (ML) and deep learning (DL) methods has provided the solution to several state-of-the-art medical problems. In this study, a novel histological dataset was created using the preparations prepared both for students in laboratory courses and obtained by ourselves in the Department of Histology and Embryology. The created dataset consists of blood, connective, epithelial, muscle, and nervous tissue. Blood, connective, epithelial, muscle, and nervous tissue preparations were obtained from human tissues or tissues from various human-like mammals at different times. Various ML techniques have been tested to provide a comprehensive analysis of performance in classification. In experimental studies, AdaBoost (AB), Artificial Neural Networks (ANN), Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM) have been analyzed. The proposed artificial intelligence (AI) framework is useful as educational material for undergraduate and graduate students in medical faculties and health sciences, especially during pandemic and distance education periods. In addition, it can also be utilized as a computer-aided medical decision support system for medical experts to minimize spent-time and job performance losses.

References

  • [1] Xu J, Luo X, Wang G, Gilmore H, Madabhushi A. Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016; 191: 214-223.
  • [2] Mazo C, Bernal J, Trujillo M, Alegre E. Transfer learning for classification of cardiovascular tissues in histological images. Computer Methods and Programs in Biomedicine 2018; 165: 69-76.
  • [3] Yang Z, Ran L, Zhang S, Xia Y, Zhang Y. EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images. Neurocomputing 2019; 366: 46-53.
  • [4] Toğaçar M, Özkurt KB, Ergen B, Cömert Z. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Statistical Mechanics and its Applications 2020; 545: 123592.
  • [5] Wang L, Jiao Y, Qiao Y, Zeng N, Yu R. A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recognition Letters 2020; 135: 244-248.
  • [6] Niemann A, Talagini A, Kandapagari P, Preim B, Saalfeld S. Tissue segmentation in histologic images of intracranial aneurysm wall. Interdisciplinary Neurosurgery 2021; 26: 101307.
  • [7] Xu H, Liu L, Lei X, Mandal M, Lu C. An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Computerized Medical Imaging and Graphics 2021; 93: 101974.
  • [8] Roberto GF, Lumini A, Neves LA, Nascimento MZ. Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images. Expert Systems with Applications 2021; 166: 114103.
  • [9] McCombe KD, Craig SG, Pulsawatdi AV, QuezadaMarín JI, Hagan M, Rajendran S, Humphries MP, Bingham V, et al. HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Computational and Structural Biotechnology Journal 2021; 19: 4840-4853.
  • [10] Sato N, Uchino E, Kojima R, Sakuragi M, Hiragi S, Minamiguchi S, Haga H, Yokoi, H, et al. Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney International Reports 2021; 6(9): 2445-2454.
  • [11] Anisuzzaman DM, Barzekar H, Tong L, Luo J, Yu Z. A deep learning study on osteosarcoma detection from histological images. Biomedical Signal Processing and Control 2021; 69: 102931.
  • [12] Kierszenbaum AL, Tres LL. Histology and cell biology: an introduction to pathology in Philadelphia. Elsevier Saunders, 2016. pp. 123, 217, 239.
  • [13] Gartner LP, Hiatt JL, Gartner LP. Color atlas and text of histology in Philadelphia: Wolters Kluwer Health/Lippincott Williams Wilkins, 2013. pp.126-148.
  • [14] Mescher AL, Junqueira LCU. Junqueira’s basic histology: Text and atlas, New York: McGraw-Hill, 2021. pp.73-98.
  • [15] Ovalle WK, Nahirney PC, Netter FH. Netter’s essential histology with correlated histopathology, 2021. pp. 51-71.
  • [16] Pawlina W, Ross MH. Histology: A Text and Atlas in Correlated Cell and Molecular Biology,2020. pp.356-404.

Year 2022, Volume 17, Issue 2, 185 - 196, 30.09.2022
https://doi.org/10.55525/tjst.1134354

Abstract

References

  • [1] Xu J, Luo X, Wang G, Gilmore H, Madabhushi A. Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016; 191: 214-223.
  • [2] Mazo C, Bernal J, Trujillo M, Alegre E. Transfer learning for classification of cardiovascular tissues in histological images. Computer Methods and Programs in Biomedicine 2018; 165: 69-76.
  • [3] Yang Z, Ran L, Zhang S, Xia Y, Zhang Y. EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images. Neurocomputing 2019; 366: 46-53.
  • [4] Toğaçar M, Özkurt KB, Ergen B, Cömert Z. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Statistical Mechanics and its Applications 2020; 545: 123592.
  • [5] Wang L, Jiao Y, Qiao Y, Zeng N, Yu R. A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recognition Letters 2020; 135: 244-248.
  • [6] Niemann A, Talagini A, Kandapagari P, Preim B, Saalfeld S. Tissue segmentation in histologic images of intracranial aneurysm wall. Interdisciplinary Neurosurgery 2021; 26: 101307.
  • [7] Xu H, Liu L, Lei X, Mandal M, Lu C. An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Computerized Medical Imaging and Graphics 2021; 93: 101974.
  • [8] Roberto GF, Lumini A, Neves LA, Nascimento MZ. Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images. Expert Systems with Applications 2021; 166: 114103.
  • [9] McCombe KD, Craig SG, Pulsawatdi AV, QuezadaMarín JI, Hagan M, Rajendran S, Humphries MP, Bingham V, et al. HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Computational and Structural Biotechnology Journal 2021; 19: 4840-4853.
  • [10] Sato N, Uchino E, Kojima R, Sakuragi M, Hiragi S, Minamiguchi S, Haga H, Yokoi, H, et al. Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney International Reports 2021; 6(9): 2445-2454.
  • [11] Anisuzzaman DM, Barzekar H, Tong L, Luo J, Yu Z. A deep learning study on osteosarcoma detection from histological images. Biomedical Signal Processing and Control 2021; 69: 102931.
  • [12] Kierszenbaum AL, Tres LL. Histology and cell biology: an introduction to pathology in Philadelphia. Elsevier Saunders, 2016. pp. 123, 217, 239.
  • [13] Gartner LP, Hiatt JL, Gartner LP. Color atlas and text of histology in Philadelphia: Wolters Kluwer Health/Lippincott Williams Wilkins, 2013. pp.126-148.
  • [14] Mescher AL, Junqueira LCU. Junqueira’s basic histology: Text and atlas, New York: McGraw-Hill, 2021. pp.73-98.
  • [15] Ovalle WK, Nahirney PC, Netter FH. Netter’s essential histology with correlated histopathology, 2021. pp. 51-71.
  • [16] Pawlina W, Ross MH. Histology: A Text and Atlas in Correlated Cell and Molecular Biology,2020. pp.356-404.

Details

Primary Language English
Subjects Engineering, Multidisciplinary
Journal Section TJST
Authors

Kübra UYAR> (Primary Author)
SELCUK UNIVERSITY, FACULTY OF TECHNOLOGY
0000-0001-5345-3319
Türkiye


Merve SOLMAZ>
SELCUK UNIVERSITY, SCHOOL OF MEDICINE
0000-0003-4144-4647
Türkiye


Sakir TASDEMIR>
SELCUK UNIVERSITY, FACULTY OF TECHNOLOGY
0000-0002-2433-246X
Türkiye


Nejat ÜNLÜKAL>
SELCUK UNIVERSITY, SCHOOL OF MEDICINE
0000-0002-8107-4882
Türkiye

Supporting Institution Selçuk Üniversitesi ve Öğretim Üyesi Yetiştirme Programı Koordinatörlüğü
Project Number 2017-OYP-047
Publication Date September 30, 2022
Published in Issue Year 2022, Volume 17, Issue 2

Cite

Bibtex @research article { tjst1134354, journal = {Turkish Journal of Science and Technology}, issn = {1308-9080}, eissn = {1308-9099}, address = {fenbilimdergi@firat.edu.tr}, publisher = {Fırat University}, year = {2022}, volume = {17}, number = {2}, pages = {185 - 196}, doi = {10.55525/tjst.1134354}, title = {A Novel Histological Dataset and Machine Learning Applications}, key = {cite}, author = {Uyar, Kübra and Solmaz, Merve and Tasdemır, Sakir and Ünlükal, Nejat} }
APA Uyar, K. , Solmaz, M. , Tasdemır, S. & Ünlükal, N. (2022). A Novel Histological Dataset and Machine Learning Applications . Turkish Journal of Science and Technology , 17 (2) , 185-196 . DOI: 10.55525/tjst.1134354
MLA Uyar, K. , Solmaz, M. , Tasdemır, S. , Ünlükal, N. "A Novel Histological Dataset and Machine Learning Applications" . Turkish Journal of Science and Technology 17 (2022 ): 185-196 <https://dergipark.org.tr/en/pub/tjst/issue/72762/1134354>
Chicago Uyar, K. , Solmaz, M. , Tasdemır, S. , Ünlükal, N. "A Novel Histological Dataset and Machine Learning Applications". Turkish Journal of Science and Technology 17 (2022 ): 185-196
RIS TY - JOUR T1 - A Novel Histological Dataset and Machine Learning Applications AU - KübraUyar, MerveSolmaz, SakirTasdemır, NejatÜnlükal Y1 - 2022 PY - 2022 N1 - doi: 10.55525/tjst.1134354 DO - 10.55525/tjst.1134354 T2 - Turkish Journal of Science and Technology JF - Journal JO - JOR SP - 185 EP - 196 VL - 17 IS - 2 SN - 1308-9080-1308-9099 M3 - doi: 10.55525/tjst.1134354 UR - https://doi.org/10.55525/tjst.1134354 Y2 - 2022 ER -
EndNote %0 Turkish Journal of Science and Technology A Novel Histological Dataset and Machine Learning Applications %A Kübra Uyar , Merve Solmaz , Sakir Tasdemır , Nejat Ünlükal %T A Novel Histological Dataset and Machine Learning Applications %D 2022 %J Turkish Journal of Science and Technology %P 1308-9080-1308-9099 %V 17 %N 2 %R doi: 10.55525/tjst.1134354 %U 10.55525/tjst.1134354
ISNAD Uyar, Kübra , Solmaz, Merve , Tasdemır, Sakir , Ünlükal, Nejat . "A Novel Histological Dataset and Machine Learning Applications". Turkish Journal of Science and Technology 17 / 2 (September 2022): 185-196 . https://doi.org/10.55525/tjst.1134354
AMA Uyar K. , Solmaz M. , Tasdemır S. , Ünlükal N. A Novel Histological Dataset and Machine Learning Applications. TJST. 2022; 17(2): 185-196.
Vancouver Uyar K. , Solmaz M. , Tasdemır S. , Ünlükal N. A Novel Histological Dataset and Machine Learning Applications. Turkish Journal of Science and Technology. 2022; 17(2): 185-196.
IEEE K. Uyar , M. Solmaz , S. Tasdemır and N. Ünlükal , "A Novel Histological Dataset and Machine Learning Applications", Turkish Journal of Science and Technology, vol. 17, no. 2, pp. 185-196, Sep. 2022, doi:10.55525/tjst.1134354