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Item REVOLUTIONIZING FOETAL CARDIAC ANOMALY DIAGNOSIS: UNLEASHING THE POWER OF DEEP LEARNING ON FOETALECHO IMAGES(Journal of Theoretical and Applied Information Technology, 2023) Divya, M O; Vijaya, M SThe use of Artificial Intelligence (AI) has amplified in various fields, with remarkable results in medicine in recent times. Despite the potential of AI in the medical field, there are still many unexplored areas due to data unavailability. One such area is cardiac foetal anomaly diagnosis, which is poorly diagnosed globally with a rate of only 50%. The complexity of the task requires a high level of expertise to understand minute hints and conduct thorough exams for accurate image captures. In this research, the FoetalEcho_V01 dataset was used for foetal cardiac anomaly diagnosis, consisting of pre-classified ultrasound images representing 15 different anomalies and a class representing normal heart images. The deep learning models which are efficient in producing potential classifiers for ultra sound scan images are identified. The models are CNN, AlexNet, VGG16 and ResNet50. The best performing deep learning models were used to produce classifiers, and their performance was evaluated. The results showed that the deep learning models performed well on the FoetalEcho_V01 dataset images for diagnosing structural cardiac anomalies in the foetus, with consistent performance as demonstrated by the calculated standard deviation. The results obtained from the research for the FetalEcho_V05 dataset are as follows. The CNN model achieved a precision of 0.94, recall of 0.89, accuracy of 0.90, and F1 score of 0.91. Comparatively, the AlexNet model demonstrated a precision of 0.92, recall of 0.87, accuracy of 0.89, and F1 score of 0.89. The VGG16 model exhibited precision of 0.91, recall of 0.85, accuracy of 0.87, and F1 score of 0.88. Lastly, the ResNet50 model displayed a precision of 0.93, recall of 0.90, accuracy of 0.93, and F1 score of 0.93. Among these models, the CNN model emerged as the best classifier for the FetalEcho_V05 dataset, with its superior performance in terms of precision, recall, accuracy, and F1 score.Item ARTIFICIAL INTELLIGENT MODELS FOR AUTOMATIC DIAGNOSIS OF FOETAL CARDIAC ANOMALIES: A META-ANALYSIS(Springer Link, 2023-01-01) Divya, M O; Vijaya, M SThe foetal anomaly scanning is one of the most challenging areas where accuracy of diagnosis much fluctuating with respect to the expertise of the radiologist and the mental equilibrium of the radiologist at the time of scanning. Amongst the various anomalies, foetal heart anomaly diagnosis expects precise and sensitive intellectual presence since perilous congenital heart diseases are one of the common causes resulting in the major population of infant mortality or into permanent natal faults. The accuracy of manual diagnosis of foetal cardiac abnormalities from the ultrasound scan images vary based on the human expertise and the presence of mind. Therefore, the scope of computer-assisted judgement can produce accurate diagnosis irrespective of the operator’s profile. Numerous researches are going on to explore the scope of computer-assisted judgement of abnormalities using ultrasound imaging technique (USIT), specifically using machine learning and deep learning models. This work exploits the opportunities of computer-assisted diagnosis in foetal cardiac anomaly diagnosis as this is one of the most sensitive areas where appropriate diagnosis can save a life and a wrong diagnosis may lose a life unnecessarily.Item OPTIMIZING PRE-PROCESSING FOR FOETAL CARDIAC ULTRA SOUND IMAGE CLASSIFICATION(Springer Link, 2023-03-28) Divya, M O; Vijaya, M SRecent research shows that Foetal cardiac anomalies which gets diagnosed postnatally makes a grave negative impact on the delivery outcome. The situation becomes lethal when severe anomalies get diagnosed after the baby is born. Many medical researches shows that delivery outcome could be better when the anomaly is diagnosed prenatally. There are hardly any research and development happening in this area where automation and prediction are on prime focus for finding the cardiac anomaly using Ultra Sound Imaging Technique (USIT). The USIT during the second trimester is universal for every pregnant woman also the second trimester is the best time to take appropriate medical assistance for the foetus in case of anomaly. This research is experimental study to setup a standard dataset for foetal cardiac anomaly USITs and to identify the appropriate pre-processing technique for binary classification of USIT. The 1200 images in the dataset are organised in two classes half of the images are with anomaly and other half without anomaly. The class with anomaly includes images representations from 17 anomalies which is theoretically established as structural anomalies of heart. All anomalies are present in the dataset approximately equal in ratio. The dataset has undergone the following pre-processing techniques, blur removal, noise removal and contrast normalisation. The Alex-net model is trained to create a binary classifier for the FetalEcho dataset after applying the different pre-processing techniques. Eight rounds of classification have been performed with eight versions of the FetalEcho dataset. The worst results were shown by the row dataset (FetalEcho_V01) when the classification experiment have been performed with the AlexNet classifier. The dataset FetalEcho_V05, created after removing blur and noise, is identified as the best performance for classification, amongst the eight datasets.