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

Senthil Kavitha, Vidyaathulasiraman

Bio-Algorithms and Med-Systems

2022

Vol. 18, no. 1

84--95

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.

Trombocytoza w raku jajnika

Tęcza K., Pamuła-Piłat J.

Laboratorium - Przegląd Ogólnopolski

2014

nr 9-10

47--50

Rak jajnika powoduje najwięcej zgonów związanych z nowotworami ginekologicznymi. Jest on najczęściej rozpoznawany w zaawansowanym stadium ze względu na brak wczesnych swoistych objawów i swoistych markerów. Wzrost liczby płytek krwi u chorych z rakiem jajnika to niekorzystny czynnik prognostyczny związany z krótszym czasem przeżycia i krótszym czasem wolnym od choroby. Trombocytoza obserwowana w raku jajnika jest możliwym parametrem diagnostyczno-prognostycznym.

Epithelial ovarian cancer is the most lethal of gynaecological malignancies. The majority of women are diagnosed in advanced stage due to non-specific symptoms of the disease and the lack of specific risk and prognostic markers. The increase of thrombocytes count in ovarian cancer patients is considered as unfavourable prognostic factor, connected with shorter overall and disease-free survival. It is possible that paraneoplastic thrombocytosis could be a new marker, useful in ovarian cancer diagnosis and prediction of patients' prognosis.

Neural networks in survival time prediction

Zambrano Quispe O., Płużańska A., Kącki E.

Journal of Applied Computer Science

2001

Vol. 9, nr 1

53-60

In this paper the results obtained by a neural network application to predict the survival time in ovarian cancer patients are presented. The neural training was performed using clinical input data from ovarian cancer patients. The results were quite encourageable, with a comparable rate of prediction with standard regression models.