A Novel Histological Dataset and Machine Learning Applications

Year 2022, Volume: 17 Issue: 2, 185 - 196, 30.09.2022

Kübra Uyar , Merve Solmaz , Sakir Tasdemır , Nejat Ünlükal

https://doi.org/10.55525/tjst.1134354

Cited By: 1

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.

Keywords

Classification, Computer-aided diagnosis, Histological image, Image processing, Machine Learning

Project Number

2017-OYP-047

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.