TY  - JOUR
TI  - Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach
AB  - Objective: Automatic machine learning methods developed by employing deep learning approaches have been the focus of numerous studies nowadays. The objective of the current study is to design a web-based software that is used in the classification of tissue samples in colorectal cancer, based on eight different histopathological tissue types, to support physicians for the clinical diagnosis of colorectal cancer, and thus to enable physicians to make quick and accurate decisions. Material and Methods: An open-access data set (DOI: 10.5281/zenodo.53169) consisting of 5,000 histopathological images, including different histopathological tissue types of colorectal cancer, was used in the present study. Keras-based AutoKeras library was applied to classify the histopathological tissue types of colorectal cancer. Appropriate python language libraries were employed in the development of the web-based software. A deep learning-based model was constructed to predict eight histopathological tissue types of colorectal cancer. Results: The highest metric values among the performance criteria achieved for different tissue types of colorectal cancer were calculated for adipose type, and we found that accuracy was 0.996, sensitivity 0.992, specificity 0.996, precision 0.974, recall 0.992, and F1-score 0.983, respectively. This research differs from other studies in that it includes open access software. Conclusion: The web software based on the model proposed in this study provided promising predictions in classifying different tissue types from histopathological images of colorectal cancer. Thanks to the proposed software, the tissue types of colorectal cancer are easily understood by medical professionals and other healthcare workers. Hence, the workload of medical professionals can be reduced, and a faster consultation system can be formed.
AU  - GÜLDOĞAN, Emek
AU  - Ucuzal, Hasan
AU  - ÇOLAK, Cemil
AU  - KÜÇÜKAKÇALI, ZEYNEP
DO  - 10.5336/biostatic.2021-82416
PY  - 2021
JO  - Türkiye Klinikleri Biyoistatistik Dergisi
VL  - 13
IS  - 2
SN  - 1308-7894
SP  - 147
EP  - 159
DB  - TRDizin
UR  - http://search/yayin/detay/491578
ER  -