ArticleDiagnostics (Basel, Switzerland)2020
Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.World journal of clinical cases · 2023Review
- Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.Diagnostics (Basel, Switzerland) · 2023Article
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- Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.New generation computing · 2023Article
- Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.Frontiers in medicine · 2023Article
- Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.Biosensors · 2022Article
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- ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.Computers in biology and medicine · 2022Article
- AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.Life (Basel, Switzerland) · 2022Article
- Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.Journal of imaging · 2021Review
- MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI.Diagnostics (Basel, Switzerland) · 2021Article
- A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset.PeerJ. Computer science · 2021Article
Corrections and comments
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3 authors.
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Abstract
The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.
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