Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

• Autorzy: Senthil Kavitha , Vidyaathulasiraman

Identyfikatory

DOI

10.1515/bams-2021-0098

• Konferencja: 4th Jagiellonian Symposium on Advances in Particle Physics and Medicine, Krakow, 10-15 July 2022
• Języki publikacji: EN

Abstrakty

EN

Objectives: This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods: The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results: Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.

Słowa kluczowe

EN

accuracy; ANN; ovarian cancer; pre-trained mask CNN; VGG-19

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2022
• Tom: Vol. 18, no. 1
• Strony: 84--95
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Senthil Kavitha

• Department of Computer Science, Periyar University, Salem, India

autor

Vidyaathulasiraman

• Department of Computer Science, Government Arts and Science College for Women, Bargur, India

Bibliografia

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Uwagi

Opublikowane przez Sciendo. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).