Medical image analysis and disease diagnosis have significantly improved with the use of AI
and Machine Learning algorithms. Automated systems for medical image analysis will help …

Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and
ultrasound examiners must address the common clinical challenge of distinguishing …

Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors
in early stage with computer-aided techniques can efficiently decrease the mortality rate …

Objective This study aimed to evaluate the performance of the deep convolutional neural
network (DCNN) to discriminate between benign, borderline, and malignant serous ovarian …

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound
images. However, clinically processed 2D ovarian-tumor ultrasound images contain many …

Ovaries are important parts of the female reproductive system because they produce the egg
or ovum needed for fertilization. Cysts frequently impact female follicles, so torsion, infertility …

Significant successes in machine learning approaches to image analysis for various
applications have energized strong interest in automated diagnostic support systems for …

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 …

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 …

This paper proposes a novel three‐dimensional convolution neural network‐based modified
bidirectional long short‐term memory with pelican optimization (3D CNN based MBiLSTM …