Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the
segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and …

This paper discusses the development of a hybrid Convolutional Neural Network (CNN)-
Support Vector Machine (SVM) model for automated breast tumor detection using ultra …

The sacred beginning of human reproduction begins with the release of an egg through
ovulation in the ovaries. Continuous monitoring of the condition of the female reproductive …

Ovarian cancer is a malignant tumor that poses a serious threat to women's lives. Computer-
aided diagnosis (CAD) systems can classify the type of ovarian tumors, but few of them can …

Ovarian carcinoma is known for its diverse subtypes with unique morphologies and clinical
characteristics, causing considerable diagnostic complexities. While deep learning has …

Objectives This paper proposed the neural network-based segmentation model using Pre-
trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian …

Abstract Machine and Deep learning has witnessed an exceptional amount of admiration in
recent years. ML has ability to learn data itself by predicting uncertain conditions or future …

In clinical diagnosis, an effective classification of ovarian carcinoma types is highly essential
to avoid the number of deaths worldwide. For this reason, deep convolutional neural …

This research presents an essential solution for classifying ultrasound diagnostic images
describing seven types of ovarian cysts: Follicular cyst, Hemorrhagic cyst, Corpus luteum …

Ovarian tumor segmentation is a challenging task that requires high accuracy. This study
presents a novel method for improving binary segmentation in ovarian ultrasound imaging …