Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …

Breast cancer detection using mammogram images at an early stage is an important step in
disease diagnostics. We propose a new method for the classification of benign or malignant …

Automated examination of biomedical signals plays a vital role to diagnose diseases and
offers useful data to several applications in the areas of physiology, sports medicine, and …

Convolutional neural networks (CNNs) show excellent performance in accurate medical
image segmentation. However, the characteristics of sample with small size and insufficient …

Ultrasound imaging (US) is one of the most common diagnostic imaging tools for producing
images of the human body in clinical practice. This work is devoted to studying ultrasound …

Automated grading of colon biopsy images across all magnifications is challenging because
of tailored segmentation and dependent features on each magnification. This work presents …

Automatic organ segmentation systems can accelerate the development of computer-aided
diagnosis (CAD) in clinical applications. In this paper, we focus on the challenging pancreas …

IoT has facilitated predominant advancements in cancer research by incorporating Artificial
intelligence (AI) that enables human decision-makers to achieve a better decision. Recently …

Objective We used texture analysis and machine learning (ML) to classify small round cell
malignant tumors (SRCMTs) and Non-SRCMTs of nasal and paranasal sinus on fat …

Background: Dermoscopic image segmentation using deep learning algorithms is a critical
technology for skin cancer detection and therapy. Specifically, this technology is a spatially …