BACKGROUND: Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However, existing registration methods often struggle to extract local features effectively, limiting thei...
OBJECTIVE: Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep lea...
In the online version of the article, a change was made in the section of author's affiliation. The affiliation of Drs. Sivakumari and Vani in the online version of the article entitled "Deep Learning-based Automated Knee Joint Localization in Radiog...
The funding details have been incorporated upon author's request in the funding section of this articles entitled "Superresolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning," 2024, 20, e15734056269264 [1]. The origi...
INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep ne...
BACKGROUND: Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms t...
AIM: The automatic computer-assisted mammogram classification system is important for women patients to detect and diagnose the cancer regions. In this work, the mammogram images are classified into three cases: healthy, benign and cancer, using the ...
INTRODUCTION: This study introduces SkinLiTE, a lightweight supervised contrastive learning model tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervi...
BACKGROUND: This research investigates the impact of pruning on reducing the computational complexity of a five-layered Convolutional Neural Network (CNN) designed for classifying MRI brain tumors. The study focuses on enhancing the efficiency of the...
INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) imag...