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Santosh Dasar

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Articles by Santosh Dasar

Explainable AI for Interpretation of Ovarian Tumor Classification Using Enhanced ResNet50

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By Srirupa Guha, Ashwini Kodipalli, Steven Fernandes, Santosh Dasar
| MDPI
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.
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Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60

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By Srirupa Guha, Ashwini Kodipalli, Steven Fernandes, Santosh Dasar
| Preprints.org
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 28 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (12:46:59 CEST) Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S.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.
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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

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By Ashwini Kodipalli, Steven Fernandes, Santosh Dasar
| MDPI
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.
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Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

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By Ashwini Kodipalli, Susheela Devi, Santosh Dasar
| Wiley Online Library Verified
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).
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