Abstract
In the realm of obstetrics, ultrasound remains a pivotal imaging technique for monitoring fetal development throughout pregnancy. This study harnesses recent strides in artificial intelligence and image processing to propose a comprehensive framework for the analysis of second-trimester ultrasound images. Our framework encompasses segmentation, computation, and estimation of fetal weight, body parts, and head measurements while considering critical parameters such as thyroid health, diabetes, high blood pressure, gestational age, and past complications. To identify ultrasound images, we devised an ensemble model that amalgamates two deep learning approaches: transfer learning for feature extraction (TLFEM) and convolutional neural networks (CNN). A comparative analysis with other deep learning algorithms underscores the effectiveness of our model. CNN-TLFEM consistently outperformed, achieving an impressive average intersection over union (mIoU) of 90% and a Dice coefficient of 96%. Furthermore, when benchmarked against three leading neural networks, our model displayed superior performance with average precision, recall, and F1 score values of 96%, 97%, and 96%, respectively.
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References
Recht MP et al (2020) Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol 30(6):3576–3584
Zhang J, Zuo H (2020) A deep RNN for CT image reconstruction. SPIE, Proc
Liao H, Lin W-A, Zhou SK, Luo J (2020) Artifact disentanglement network for unsupervised metal artifact reduction. IEEE Trans Med Imaging 39(3):634–643
Bi X, Li S, Xiao B, Li Y, Wang G, Ma X (2020) Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology. Neurocomputing 392:296–304
Kazeminia S et al (2020) GANs for medical image analysis. Artif Intell Med 109:101938
Reddy AVN, Krishna CP, Mallick PK et al (2020) Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks. J Big Data 7:35. https://doi.org/10.1186/s40537-020-00311-y
Kaur M, Singh D (2020) Fusion of medical images using deep belief networks. Cluster Comput 23(2):1439–1453
Zhu J, Li Y, Hu Y, Ma K, Zhou SK, Zheng Y (2020) Rubik’s Cube+: a self-supervised feature learning framework for 3D medical image analysis. Med Image Anal 64:101746
Azizi S et al (2021) Big self-supervised models advance medical image classification. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, pp 3458–3468. https://doi.org/10.1109/ICCV48922.2021.00346
Zhai X, Rajaram A, Ramesh K (2022) Cognitive Model for Human Behavior Analysis. J Interconnect Netw 22(Supp04):2146013
Indira DNVSLS, Ganiya RK, Ashok Babu P, Xavier A, Kavisankar L, Hemalatha S, Yeshitla A (2022) Improved artificial neural network with state order dataset estimation for brain cancer cell diagnosis. Biomed Res Int 7799812. https://doi.org/10.1155/2022/7799812
Liu Q, Yu L, Luo L, Dou Q, Heng PA (2020) Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans Med Imaging 39(11):3429–3440
Ali S (2022) Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 5(1):184
Qiao D, Zulkernine F (2020) Dilated squeeze-and-excitation U-Net for fetal ultrasound image segmentation. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, pp 1–7
Ajilisa OA, Jagathy Raj VP, Sabu MK (2022) Segmentation of thyroid nodules from ultrasound images using convolutional neural network architectures. J Intell Fuzzy Syst 43(1):687–705
Bushra SN, Shobana G (2021) Obstetrics and gynaecology ultrasound image analysis towards cryptic pregnancy using deep learning-a review. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), (pp 949–953 IEEE
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Keerthi, G., Abirami, M.S. Intelligent diagnosis of fetal organs abnormal growth in ultrasound images using an ensemble CNN-TLFEM model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18561-w
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DOI: https://doi.org/10.1007/s11042-024-18561-w