A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm

Abstract

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

“…These challenges can be mitigated through the incorporation of techniques such as regularization parameter tuning and the strategic use of skip connections, as exemplified by ResNets. The inclusion of skip connections not only alleviates the vanishing gradient problem but also [58,[95][96][97][98][99][100][101][102][103]107,109,[115][116][117]120,[124][125][126][127][128][129][130][135][136][137]141,142,145,150,153,157,160,161,169]. Each study's color code reflects its relevance to a certain organ, including the heart, brain, lung, or analyzing for chromosomal abnormalities.…”

“…Each entry provides information about the employed methods, total number of images, key performance metrics, and application domain. [58,[95][96][97][98][99][100][101][102][103]107,109,[115][116][117]120,[124][125][126][127][128][129][130][135][136][137]141,142,145,150,153,157,160,161,169]. Each study's color code reflects its relevance to a certain organ, including the heart, brain, lung, or analyzing for chromosomal abnormalities.…”

“…They collected 100 fetal US images to evaluate for anomalies. According to the authors, the accuracy of their model achieved 98.64% precision for classifying anomalies linked with NT thickness [157]. The previously mentioned Lin et al model was also capable of NT identification [126].…”

A novel adaptive sampling algorithm for cyber-physical systems

“…In [30], the authors proposed an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality, the findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. In [2], the authors proposed an AI-enabled IoT-CPS Algorithm which doctors can utilise to discover diseases in patients based on AI.…”

Pedestrian target detection algorithm based on improved YOLO v5

“…The improved YOLO V5s algorithm in this paper uses Python language and Pytorch deep learning framework to structure and program the network model, and uses the stochastic gradient descent (SGD [11] ) algorithm to optimize the parameters of the model during training. In the training process, we set the momentum as 0.6, the initial learning rate as 0.01, 10 training times as a cycle, the learning rate of each cycle decreases by 0.01, and the weight decreases by 0.0002.…”