Detection, Localization and Classification of Fetal Brain Abnormalities using YOLO v4 Architecture

doi:10.23940/ijpe.22.10.p5.720-729

Abstract

Abstract: In the medical field, the intensifying use of Artificial Intelligence and Machine Learning tends to lower the time and increase accuracy in detecting illnesses in their early stages. AI will have a 10.4 percent influence on the Indian economy in 2030, amounting to 0.9 trillion dollars. More than 80 anomalies in a child's fetal development have been discovered today. As it takes more time to manually identify the lesion tissues in the fetal brain, this work proposes an automatic detection and classification of fetal brain abnormalities using a cloud environment. The art of finding the abnormalities in the fetal brain is the core objective of the system, which eradicates or reduces the time and cost and improves the accuracy. The Machine Learning algorithm is introduced in fetal MRI scans to discover and pinpoint fetal brain anomalies. The Precise Epic Localization Algorithm is adopted in YOLO v4 architecture to detect and classify the healthy fetal brain with its orientation and unhealthy brain with their abnormalities from the given input of MRI Images. In this proposed work, the detection and classification of Encephalocele and Arteriovenous Malformation from a fetal brain MRI are obtained and evaluated using a machine learning algorithm to determine the abnormalities with the accuracy of 97.27%, which outperforms the public tools, BET and ROBEX. As Tesla P100 GPU is employed in the cloud environment, the output is more convenient and accessible than the existing methods.

Key words: fetal brain detection, fetal brain classification, fetal brain localization, precise epic localization (PEL) algorithm

N. Suresh Kumar, and Amit Kumar Goel. Detection, Localization and Classification of Fetal Brain Abnormalities using YOLO v4 Architecture [J]. Int J Performability Eng, 2022, 18(10): 720-729.

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References

1. Strickland E.The Turbulent past and Uncertain Future of AI: Is There a Way Out of AI’s Boom-and-Bust Cycle?. IEEE Spectrum, vol. 58, no, 10, pp. 26-31, 2021.
2. Wang L., Sarker P., Alam K., andSumon S.Artificial Intelligence and Economic Growth: A Theoretical Framework. Scientific Annals of Economics and Business, vol. 68, no, 4, pp. 421-443, 2021.
3. Van Dyck, L.E., Kwitt, R., Denzler, S.J., and Gruber, W.R. Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—an Eye Tracking Study. Frontiers in Neuroscience, vol. 15, pp. 750639, 2021.
4. Madhiarasan, M. and Louzazni, M.Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications. Journal of Electrical and Computer Engineering, 2022.
5. Song, J. and Chen, Y.A Study on the Application and the Advancement of Deep Neural Network Algorithm. In Journal of Physics: Conference Series, IOP Publishing, vol. 2146, no. 1, pp. 012001, 2022.
6. Liu L., Wang Y., andChi W.Image Recognition Technology based on Machine Learning. IEEE Access, 2020.
7. Cao D., Chen Z., andGao L.An Improved Object Detection Algorithm based on Multi-Scaled and Deformable Convolutional Neural Networks. Human-centric Computing and Information Sciences, vol. 10, no. 1, pp.1-22, 2020.
8. Pang B., Nijkamp E., andWu Y.N.Deep Learning with Tensorflow: A Review. Journal of Educational and Behavioral Statistics, vol. 45, no. 2, pp. 227-248, 2020.
9. Kumar, N.S. and Kumar, T.A Civilized Method to Fetal Brain Segmentation with U-Net Architecture using Optimal Semantic Blend Algorithm. International Journal on Emerging Technologies, vol. 11, no. 2, pp. 187-191, 2020.
10. Pandey M., Fernandez M., Gentile F., Isayev O., Tropsha A., Stern A.C., andCherkasov A.The Transformational Role of GPU Computing and Deep Learning in Drug Discovery. Nature Machine Intelligence, vol. 4, no. 3, pp. 211-221, 2022.
11. Khalili N., Lessmann N., Turk E., Claessens N., de Heus, R., Kolk, T., Viergever, M.A., Benders, M.J., and Išgum, I. Automatic Brain Tissue Segmentation in Fetal MRI using Convolutional Neural Networks. Magnetic resonance imaging, vol. 64, pp. 77-89, 2019.
12. Kornilov A., Safonov I., andYakimchuk I.A Review of Watershed Implementations for Segmentation of Volumetric Images. Journal of Imaging, vol. 8, no. 5, pp. 127, 2022.
13. Kumar, N.S. and Goel, A.K. An Optimized Approach to Clinical Object Identification using YOLO v3 in the Cloud Environment. In2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, pp. 856-859, 2021.
14. Ebner M., Wang G., Li W., Aertsen M., Patel P.A., Aughwane R., Melbourne A., Doel T., Dymarkowski S., De Coppi, P., and David, A.L. An Automated Framework for Localization, Segmentation and Super-Resolution Reconstruction of Fetal Brain MRI. NeuroImage, vol. 206, pp. 116324, 2020.
15. Dong J., Li Z., Wang Z., Wang N., Guo W., Ma D., Hu H., andZhong S.Pixel-Level Intelligent Segmentation and Measurement Method for Pavement Multiple Damages based on Mobile Deep Learning. IEEE Access, vol. 9, pp. 143860-143876, 2021.
16. Zhou W., Gao S., Zhang L., andLou X.Histogram of Oriented Gradients Feature Extraction from Raw Bayer Pattern Images. IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 5, pp. 946-950, 2020.
17. Kumar S. J.N., Kumar, N. S., Kumar, A. S., Kousik, N. V., and Kavithaa, G. A New Security Mechanism for VCC based on the Newly Modified Structure of DES-3L Algorithm using FC System, European Journal of Molecular & Clinical Medicine, vol. 7, no. 8, pp. 2026-2034, 2020.
18. Koley S., Dutta P.K., andAganj I.Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images. Scientific Reports, vol. 11, no. 1, pp. 1-21, 2021.
19. Somasundaram K., Gayathri S.P., Shankar R.S., andRajeswaran R. Fetal Head Localization and Fetal Brain Segmentation from MRI using the Center of Gravity. In2016 International Computer Science and Engineering Conference (ICSEC), IEEE, pp. 1-6, 2016.
20. Albahli S., Nida N., Irtaza A., Yousaf M.H., andMahmood M.T.Melanoma Lesion Detection and Segmentation using YOLOv4-DarkNet and Active Contour. IEEE Access, vol. 8, pp. 198403-198414, 2020.
21. Chandana, C. and Parthasarathy, G.Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer. International Journal of Performability Engineering, vol. 18, no. 1, 2022.
22. Arora V., Sidhu B.S., andSingh K.Comparison of Computed Tomography and Magnetic Resonance Imaging in Evaluation of Skull Lesions. Egyptian Journal of Radiology and Nuclear Medicine, vol. 53, no. 1, pp. 1-12, 2022.
23. Ji J., Lu X., Luo M., Yin M., Miao Q., andLiu X.Parallel Fully Convolutional Network for Semantic Segmentation. IEEE Access, vol. 9, pp. 673-682, 2020.
24. Li B., Jiang W., Gu J., Liu K., andWu Y. Research on Convolutional Neural Network in the Field of Object Detection. In2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), IEEE, pp. 820-827, 2020.
25. Girshick R.Fast R-CNN. In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
26. Dong R., Xu D., Zhao J., Jiao L., andAn J.Sig-NMS-based Faster R-CNN Combining Transfer Learning for Small Target Detection in VHR Optical Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 8534-8545, 2019.
27. Liu Q., Fan X., Xi Z., Yin Z., andYang Z.Object Detection based on Yolov4-Tiny and Improved Bidirectional Feature Pyramid Network. In Journal of Physics: Conference Series, IOP Publishing, vol. 2209, no. 1, pp. 012023, 2022.
28. Nepal, U. and Eslamiat, H.Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs. Sensors, vol. 22, no. 2, pp. 464, 2022.
29. Rutherford S., Sturmfels P., Angstadt M., Hect J., Wiens J., van den Heuval, M., Scheinost, D., Thomason, M., and Sripada, C. Automated Brain Masking of Fetal Functional MRI. bioRxiv, pp. 525386, 2019.
30. Guerreiro J., Ilic A., Roma N., andTomas P.GPU Static Modeling using PTX and Deep Structured Learning. IEEE Access, vol. 7, pp. 159150-159161, 2019.
31. El Tecle, N. E., Abdelsalam, S.T. Griffin, N. Quadri, and J. R. Coppens, Arteriovenous Malformation of The Brain, Curr. Clin. Neurol., pp. 169-191, 2022.
32 32. Cruz, A.J.M. and De Jesus, O. Encephalocele. In StatPearls [Internet]. StatPearls Publishing, 2021.