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Birth weight is an important indicator of fetal development, which directly influencing the health and safety of both mother and child. However, accurately predicting fetal growth remains a challenging task due to complex influencing factors. To overcome this issue, this paper proposes a new framework called Multidirectional Perception Generative Adversarial Network with Rat Swarm Optimizer for Fetal Growth Prediction (MPGAN-RSO-FGP) to enhance birth weight predictions. The model integrates the capabilities of the Multidirectional Perception Generative Adversarial Network (MPGAN) with the Rat Swarm Optimizer (RSO) to optimize prediction accuracy. Input parameters, including gestational age and birth weight are categorized into three sets: (i) Small for Gestational Age (SGA), (ii) Appropriate for Gestational Age (AGA), (iii) Large for Gestational Age (LGA). In general, the MPGAN does not adopt any optimization strategy to determine the optimal parameters. That’s why, RSO is used to optimize the MPGAN for accurate fetal growth prediction. The proposed MPGAN-RSO-FGP is evaluated using performance metrics, such as Accuracy, Mean Relative Error (MRE), F-score, Precision, Sensitivity, Specificity, ROC, Computational time. The experimental results exemplify that the MPGAN-RSO-FGP outperforms existing models. The MPGAN-RSO-FGP attains 20.78%, 23.67%, and 17.98% higher accuracy, and 21.98%, 23.56%, and 30.78% higher precision compared to the existing LSTM-FBWP, SVM-PSGA, and RF-PLBW models. These findings demonstrate the model’s significant impact on decision-making systems, providing more reliable and efficient fetal growth predictions, which can aid in timely clinical interventions and improve maternal-infant outcomes. [ABSTRACT FROM AUTHOR]

In the present research, we describe a computer-aided detection (CAD) method aimed at automatic fetal head circumference (HC) measurement in 2D ultrasonography pictures during all trimesters of pregnancy. The HC might be utilized toward determining gestational age and tracking fetal development. This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited. The CAD system is divided into two steps: to begin, Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull. We identified the HC using dynamic programming, an elliptical fit, and a Hough transform. The computer-aided detection (CAD) program was well-trained on 999 pictures (HC18 challenge data source), and then verified on 335 photos from all trimesters in an independent test set. A skilled sonographer and an expert in medicine personally marked the test set. We used the crown-rump length (CRL) measurement to calculate the reference gestational age (GA). In the first, second, and third trimesters, the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7 ± 2.7, 0.0 ± 4.5, and 2.0 ± 12.0 days, respectively. The regular duration variance between the baseline GA and the health investigator's GA remained 1.5 ± 3.0, 1.9 ± 5.0, and 4.0 ± 14 a couple of days. The mean variance between the standard GA and the CAD system's GA remained between 0.5 and 5.0, with an additional variation of 2.9 to 12.5 days. The outcomes reveal that the computer-aided detection (CAD) program outperforms an expert sonographer. When paired with the classifications reported in the literature, the provided system achieves results that are comparable or even better. We have assessed and scheduled this computerized approach for HC evaluation, which includes information from all trimesters of gestation. [ABSTRACT FROM AUTHOR]