Detection and Classification of Pregnancy StateUsing Deep Learning Technique

Hassan  Abdelrhman Mohammed; Eltahir  Mohmmed Hussein; Mahir  Mohammed Sharif

doi:10.52981/oiuj.v17i2.1819

Hassan Abdelrhman Mohammed 1Dept. of Computer Sciences Faculty of Computer Sciences & Information Technology, Karary University, Khartoum,Sudan
Eltahir Mohmmed Hussein 2Dept of Electrical and Electronic Engineering, Faculty of Engineering Sudan University of Science &Technology,Khartoum, Sudan
Mahir Mohammed Sharif 3Dept. of Computer Science Faculty of Computer Science & Information Technology, Omdurman Islamic University, Omdurman, Sudan

DOI: https://doi.org/10.52981/oiuj.v17i2.1819

Keywords: UltrasoundImage, Recognition, etusdetection, Deep Learning &CNN.

Abstract

This work aims to design and develop a model that detects and classifies pregnancy health status. Ultrasound is one of the most prevalent developments in clinical imaging, as it enables a doctor to evaluate, analyze and treat diseases. Most complications from pregnancy lead to serious problems that restrict healthy growth, causing weakness or death. In this work, an image processing system was developed to recognize the health during pregnancy and classify it for all stages of its development. The technique in deep learning has been implemented, as CNN (Resnet50) image recognition model was applied to detect and classify fetal health status from ultrasound images. The proposed model contributed to providing an integrated solution for each pregnancy period that works to identify all stages of fetal development, starting from the pre-pregnancy stage (here it is known about the suitability of the uterus for pregnancy, the size of the ovum, and its ability to form the fetus) and up to the stage of birth, through training, verification and testing using the cross-verification technique that five folds of the diagnostic rudder were used under the patterns that distinguish each stage from the other and to verify that it is sound or unsound in the concerning stage. This study enhanced diagnostic accuracy by using transfer learning and novel accessory images that were not trained as feedback. The model achieved an accuracy of 96.5% in detecting the fetus and classifying it into any of the stages that were divided according to the features that appear from one stage to the next to eleven categories.

Author Biographies

Hassan Abdelrhman Mohammed, 1Dept. of Computer Sciences Faculty of Computer Sciences & Information Technology, Karary University, Khartoum,Sudan

^{1Dept. of Computer Sciences Faculty of Computer Sciences & Information Technology, Karary University, Khartoum,Sudan}

Eltahir Mohmmed Hussein, 2Dept of Electrical and Electronic Engineering, Faculty of Engineering Sudan University of Science &Technology,Khartoum, Sudan

^{Dept of Electrical and Electronic Engineering, Faculty of Engineering Sudan University of Science &Technology,Khartoum, Sudan}

Mahir Mohammed Sharif, 3Dept. of Computer Science Faculty of Computer Science & Information Technology, Omdurman Islamic University, Omdurman, Sudan

^{Dept. of Computer Science Faculty of Computer Science & Information Technology, Omdurman Islamic University, Omdurman, Sudan}

References

Al-Bander, B., Alzahrani, T., Alzahrani, S., Williams, B. M., & Zheng, Y. (2019). Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. Paper presented at the Annual Conference on Medical Image Understanding and Analysis.

Attallah, O., Sharkas, M. A., & Gadelkarim, H. (2020). Deep learning techniques for automatic detection of embryonic neurodevelopmental disorders. Diagnostics, 10(1), 27.

Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., . . . Rueckert, D. (2017). SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE transactions on medical imaging, 36(11), 2204-2215.

Deressa, T. D., & Kadam, K. (2018). Prediction of fetal health state during pregnancy: a survey. Int. J. Comput. Sci. Trends Technol.(IJCST), 6(1).

Diniz, P. H. (2020). Deep Learning Strategies for Ultrasound in Pregnancy. REPRODUCTIVE HEALTH.

Grandjean, G. A., Hossu, G., Banasiak, C., Ciofolo-Veit, C., Raynaud, C., Rouet, L., . . . Beaumont, M. (2019). Optimization of Fetal Biometry With 3D Ultrasound and Image Recognition (EPICEA): protocol for a prospective cross-sectional study. BMJ open, 9(12), e031777.

Ishikawa, G., Xu, R., Ohya, J., & Iwata, H. (2019). Detecting a Fetus in Ultrasound Images using Grad CAM and Locating the Fetus in the Uterus. Paper presented at the ICPRAM.

Luo, H., Liu, H., Li, K., & Zhang, B. (2019). Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning. arXiv preprint arXiv:1912.05260.

Mu, Y., Feng, K., Yang, Y., & Wang, J. (2018). Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data. Paper presented at the MATEC Web of Conferences.

Qu, R., Xu, G., Ding, C., Jia, W., & Sun, M. (2019). Deep learning-based methodology for recognition of fetal brain standard scan planes in 2D ultrasound images. IEEE Access, 8, 44443-44451.

Rawat, V., Jain, A., & Shrimali, V. (2018). Automated techniques for the interpretation of fetal abnormalities: a review. Applied bionics and biomechanics, 2018.

Sobhaninia, Z., Emami, A., Karimi, N., & Samavi, S. (2020). Localization of fetal head in ultrasound images by multiscale view and deep neural networks. Paper presented at the 2020 25th International Computer Conference, Computer Society of Iran (CSICC).

Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., & Soroushmehr, S. R. (2019). Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Paper presented at the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Tahir, M., Badriyah, T., & Syarif, I. (2018). Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization. EMITTER International Journal of Engineering Technology, 6(2), 236-253.

Tran, D., Cooke, S., Illingworth, P., & Gardner, D. (2019). Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Human Reproduction, 34(6), 1011-1018.

Wang, S., Hua, Y., Cao, Y., Song, T., Xue, Z., Gong, X., . . . Guan, H. (2018). Deep learning based fetal middle cerebral artery segmentation in large-scale ultrasound images. Paper presented at the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Zhang, J., Petitjean, C., Yger, F., & Ainouz, S. (2020). Explainability for regression CNN in fetal head circumference estimation from ultrasound images. In Interpretable and Annotation-Efficient Learning for Medical Image Computing (pp. 73-82): Springer.