Artificial intelligence (AI) has gained prominence in medical imaging, particularly in
obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is …

Background and objectives: By extracting this information, Machine or Deep Learning
(ML/DL)-based autonomous data analysis tools can assist clinicians and cancer researchers …

Breast ultrasound (BUS) image classification in benign and malignant classes is often based
on pre-trained convolutional neural networks (CNNs) to cope with small-sized training data …

Hemorrhage is the leading cause of preventable death in both civilian and military medicine.
Junctional hemorrhages are especially difficult to manage since traditional tourniquet …

The timely identification and management of ovarian cancer are critical determinants of
patient prognosis. In this study, we developed and validated a deep learning radiomics …

Background: Histopathology is a gold standard for cancer diagnosis. It involves extracting
tissue specimens from suspicious areas to prepare a glass slide for a microscopic …

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

Introduction Military working dogs (MWDs) are essential for military operations in a wide
range of missions. With this pivotal role, MWDs can become casualties requiring specialized …

Background Accurate preoperative identification of ovarian tumour subtypes is imperative for
patients as it enables physicians to custom-tailor precise and individualized management …

Currently, the advent of CNN (Convolutional Neural Network) has brought very convincing
results to computer vision problems. One-stage CNNs are a suitable choice for research and …