![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Deep Learning Algorithms for Detection and Classification of Congenital Brain Anomaly
Subscribe/Renew Journal
Congenital brain anomalies are structural abnormalities that occur during fetal development and can have a significant impact on an individual neurological function. Detecting and classifying these anomalies accurately and efficiently is crucial for early diagnosis, intervention, and treatment planning. In recent years, recurrent neural networks (RNNs) have emerged as powerful tools for analyzing sequential and time-series data in various domains, including medical imaging. This research presents an overview of RNN-based algorithms for the detection and classification of congenital brain anomalies. Specifically, Long Short-Term Memory (LSTM) networks and Convolutional LSTM networks have demonstrated great potential in this domain. LSTMs excel at capturing long-range dependencies in sequential data and mitigating the vanishing gradient problem, making them well-suited for analyzing brain scans or other medical imaging sequences. Convolutional LSTM networks combine the strengths of convolutional neural networks (CNNs) and LSTMs, enabling them to extract spatial features from brain images while preserving temporal dependencies. The application of RNN algorithms in the detection and classification of congenital brain anomalies shows promising results, enabling accurate and timely identification of these abnormalities. However, further research is needed to validate and refine these algorithms, improve their interpretability, and enhance their clinical utility in real-world scenarios.
Keywords
Deep Learning, Brain Anomalies, Automation, Diagnosis, MRI.
Subscription
Login to verify subscription
User
Font Size
Information
- H.N. Xie and Y.Z. Yang, “Using Deep‐Learning Algorithms to Classify Fetal Brain Ultrasound Images as Normal or Abnormal”, Ultrasound in Obstetrics and Gynecology, Vol. 56, No. 4, pp. 579-587, 2020.
- F.Q. Kareem and A.M. Abdulazeez, “Ultrasound Medical Images Classification based on Deep Learning Algorithms: A Review”, Fusion: Practice and Applications, Vol. 3, No. 1, pp. 29-42, 2021.
- O. Attallah, M.A. Sharkas and H. Gadelkarim, “Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age”, Brain Sciences, Vol. 9, No. 9, pp. 231-239, 2019.
- E. Cainelli and L. Vedovelli, “Detecting Neurodevelopmental Trajectories in Congenital Heart Diseases with a Machine-Learning Approach”, Scientific Reports, Vol. 11, No. 1, pp. 1-11, 2021.
- V.A. Mohammed, M.A. Mohammed, M.A. Mohammed and N. Jiwani, “Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder”, Proceedings of International Conference on Artificial Intelligence and Smart Energy, pp. 1022-1028, 2023.
- R.K. Nayak and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- S. Gupta and K. Babu, “Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer Disease-based Neurodegenerative Disorders”, Computational and Mathematical Methods in Medicine, Vol. 2022, pp. 1-15, 2022.
- S. Gofer, Y. Gilboa and S. Perlman, “Machine Learning Algorithms for Classification of First‐Trimester Fetal Brain Ultrasound Images”, Journal of Ultrasound in Medicine, Vol. 41, No. 7, pp. 1773-1779, 2022.
- S. Kannan, P. Arulprakash and P. Dhinakar, “Analysis of Convolutional Recurrent Neural Network Classifier for COVID-19 Symptoms over Computerised Tomography Images”, International Journal of Computer Applications in Technology, Vol. 66, No. 3-4, pp. 427-432, 2021.
- M.R. Boland and N.P. Tatonetti, “Development of a Machine Learning Algorithm to Classify Drugs of Unknown Fetal Effect”, Scientific Reports, Vol. 7, No. 1, pp. 12839-12845, 2017.
- V.A. Mohammed, M.A. Mohammed, M.A. Mohammed and N. Jiwani, “An Earlier Serial Lactate Determination Analysis of Cardiac Arrest Patients using a Medical Machine Learning Model”, Proceedings of International Conference on Intelligent Systems for Communication, IoT and Security, pp. 263-268, 2023.
- J. Zhu and J. Ji, “Brain Tissue Development of Neonates with Congenital Septal Defect: Study on MRI Image Evaluation of Deep Learning Algorithm”, Pakistan Journal of Medical Sciences, Vol. 37, No. 6, pp. 1652-1662, 2021.
- R. Qu, W. Jia and M. Sun, “Deep Learning-Based Methodology for Recognition of Fetal Brain Standard Scan Planes in 2D Ultrasound Images”, IEEE Access, Vol. 8, pp. 44443-44451, 2019.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 144
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0