Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images. (7th October 2020)

• Record Type: Journal Article
• Authors: Deepika, P.; Suresh, R.M.; Pabitha, P.
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 128
• Page End: 154
• Publication Date: 2020-10-07
• Subjects: convolutional neural network -- deep learning -- Down syndrome analysis -- fetus diagnosis -- nuchal translucency; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12394
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: One of the most important industries which protect human from various diseases is the medical industry. Child death is a crucial concern that needs to concentrate on "save the children." Abnormality of a child can be obtained by diagnosing the prenatal by ultrasound system within a specific period for providing better treatment to do "save the children.". This article aimed to diagnose the (prenatal) ultrasound‐images by design and implement a novel framework named Defending Against Child Death (DACD). The existing method is a semiautomatic method where it used convolutional neural network (CNN) algorithm for classifying ultrasound images. Real‐time medical industry requires a fully automatic method for classifying the ultrasound images to save the human. Hence this article, includes deep learning by implementing five convolutional neural network architectures in an order where it learns, estimate, and confirms the fetus parameters. All the layers in the convolutional neural network extract and classify the different number of features in the ultrasound images automatically and provide a result. The increased number of hidden layers in the CNN can extract even the hidden features of the images. The extracted features are classified automatically and improve the accuracy of disease detection. To segment the fetus abdomen, U‐Net architecture is included in the CNN with Hough transformation. The experiment is carried out using the CNN toolbox in MATLAB and the outcomes are verified. The performance of the DACD is assessed by comparing the results with the earlier researches. From the experimental results, it is obtained that the accuracy of DACD is 99.7%, which is higher than the results obtained from the existing machine learning approach.