Browsing by Author "kumar, Ankit"

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    An effective method for predicting postpartum haemorrhage using deep learning techniques
    (Springer, 2022) Kumar, V. D. Ambeth; Ruphitha, S.V.; Kumar, Abhishek; kumar, Ankit; Raja, Linesh; Singhal, Achintya
    Postpartum haemorrhage is a type of blood loss that occurs after the birth of a baby. When you lose more than 500�ml of blood, your blood pressure drops, and you may suffer and die as a result. Deep learning techniques can predict postpartum hemorrhage earlier. As a result, we would be able to save the human. This paper discusses various types of deep learning techniques. This paper focuses on the concept of Convolutional neural networks and divides it into two sections: ZFnet and VGG-16net. By comparing the results of two nets, we can determine which of the techniques is best for predicting postpartum hemorrhage at an earlier stage. This study will be more beneficial to pregnant women in the future. The paper focuses on two nets that are said to be more useful and to be a standardized technique that also helps to give relevant medicine to patients at the appropriate time. In this paper, the algorithm is used for the VGG-16net, and the Confusion matrix is used for both nets to improve performance. Many metrics are used in this research to improve accuracy and results. Finally, the convolutional neural network concept of VGG-16net produced better results than ZF-net. � 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.