Detecting and Classifying Fetal Brain Abnormalities Using Machine Learning Techniques

Attallah, Omneya; Gadelkarim, Heba; Sharkas, Maha

doi:10.1109/icmla.2018.00223

Cited by 43 publications

(26 citation statements)

References 18 publications

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“…The results in the table show that the proposed framework is competitive with other methods based on standard machine learning techniques. The highest accuracy achieved using the proposed framework was 88.6% which was greater than that achieved in Reference [10], but slightly lower than that achieved in Reference [11].…”

Section: Comparison With Related Workcontrasting

confidence: 55%

“…As long as the amount of data increases, DCNNs outperform standard machine learning techniques. Our approach outperformed the method proposed in Reference [10]. However, in some cases, when the dataset was relatively small, standard machine learning techniques might outperform deep learning methods [36,37], which was the case when comparing with Reference [11].…”

Section: Discussionmentioning

confidence: 71%

“…However, the use of machine learning and deep learning techniques was not extensively explored. There are a limited number of research articles [8][9][10][11] that investigated the use of such techniques for detecting ENDs. These articles utilized conventional machine learning methods for feature extraction and the classification of ENDs.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML) approaches are used extensively in medical applications due to their interesting capability for extracting valuable information from medical datasets [7]. Previous work done in embryonic neurodevelopmental disorder classification (ENDC) is very limited [8][9][10][11]. These articles applied standard machine learning methods to MRI images for feature extraction and classification of embryonic neurodevelopmental disorders.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, classifying neurodevelopmental disorders of the brain of the embryo was not extensively investigated, and very limited work was done to solve this problem. Such related work is limited to References [8][9][10][11] only. To our own knowledge, this is the first study that uses deep learning techniques to detect ENDs.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

2020

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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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Section: Comparison With Related Workcontrasting

confidence: 55%