Apr 30, 2024 · The suggested deep learning-based system effectively detects the presence of the essential cerebral structure known as Nuchal Translucency, hence aiding in the diagnosis of Down's syndrome.

The suggested deep learning-based system effectively detects the presence of the essential cerebral structure known as Nuchal Translucency, hence aiding in the diagnosis of Down's syndrome. A dataset comprising more than 1100 pre-processed 2D …

Objectives: This review intends to identify methodologies and technologies used in AI-driven DS diagnostics. It evaluates the performance of AI models in terms of standard evaluation metrics, highlighting their strengths and limitations.

Apr 30, 2024 · The application of deep learning and machine learning techniques has significantly enhanced the diagnosis. In order to detect or anticipate conditions like Down syndrome, it assesses the intercranial structures of the developing …

Sep 1, 2023 · This study develops a Down-Syndrome-Detector (DSD) architecture based on the Transformer structure, which includes a segmentation module, an alignment module, a classification module, and a Down Syndrome indicator.

Dec 9, 2024 · Down Syndrome (DS) is the genetical disorder which can be screened by the ultrasound fetus images. This research work proposes an automated ultrasound fetus image classification system using DL algorithm.

on the modern Transformer architecture for the detection of Down Syndrome from original metaphase micrographs. Both segmentation and classification models developed in this study are assessed using public datasets with com-monly used metrics, and both achieved good results.

To overcome this problem, we have proposed an intelligent method based on PSO to find the optimal architecture of the CNN. The main structure of the proposed method is shown in Figure 2. FP is the number of incorrect predictions that an instance sitive. Table 3. Confusion matrix of conventional ConvNet for the best obtained result. Table 5.

We evaluate the performance of several up-to-date convolution neural network (CNN) architectures, namely ResNet-50, VGG-16, and DenseNet-121, then compare their results with traditional machine learning classifiers.

... overview of the proposed Down-Syndrome-Detector (DSD) workflow is given in Fig. 3. From left to right: (1) The first (blue) component is the Segmentation Module of DSD which recognizes each...