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Articles by Ashwini Kodipalli
Explainable AI for Interpretation of Ovarian Tumor Classification Using Enhanced ResNet50
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 28 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (12:46:59 CEST) Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S. Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints 2024, 2024051865. https://doi.org/10.20944/preprints202405.1865.v1 Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S.
Diagnostics | Free Full-Text | An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model
REFERENCES , & (2023). Prediction of polycystic ovarian syndrome using machine learning techniques. In Machine learning, image processing, network security and data sciences: Select proceedings of 3rd international conference on MIND 2021 (pp. 53–63). Springer Nature Singapore. , , & (2020). Diagnosis of polycystic ovary syndrome using machine learning algorithms. In IEEE region 10 symposium (TENSYMP) (pp. 1486–1489). IEEE. , , , , & (2021).
Sensitivity analysis of physical and mental health factors affecting Polycystic ovary syndrome in women
Abstract Polycystic ovary syndrome (PCOS) is a common condition affecting women worldwide. In this paper, we analyse physical and mental health factors affecting PCOS in women. We collected data in the form of questionnaires from women belonging to diverse backgrounds and performed a sensitivity analysis to find which factors are significant in determining the presence or absence of PCOS in women and further predict the severity of the symptoms.
Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks
4.2. Segmentation Results Using the UNet Model The performance metrics used to evaluate the segmentation process were Dice and Jaccard scores. The loss function used in the UNet was Softmax. Dice score: This metric was used to determine the similarity between two images. Here, two images refer to the ground truth and the segmented image.
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