Academic Articles & Research Papers

Computer vision and machine‐learning techniques for quantification and predictive modeling of intracellular anticancer drug delivery by nanocarriers

Abstract The field of cancer nanomedicine has made significant progress, but its clinical translation is impeded by many challenges, such as the difficulty in analyzing intracellular anticancer drug release by the nanocarriers due to the la...

Non-Destructive Monitoring of External Quality of Date Palm Fruit (<i>Phoenix dactylifera</i> L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

The emergence of new technologies focusing on “computer vision” has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh...

Geometric modeling for computer vision.

Abstract : The main contribution of this thesis is the development of a three dimensional geometric modeling system for application to computer vision. In computer vision geometric models provide a goal for descriptive image analysis, an or...

Applications of deep learning to MRI images: A survey

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...

Applications of deep learning in fundus images: A review

Deep Learning in Microscopy Image Analysis: A Survey

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...

Review of deep learning for photoacoustic imaging

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began...

Deep learning applications in ophthalmology

PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology. RECENT FINDINGS: Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the...

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...

Deep Learning and Medical Diagnosis: A Review of Literature

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 ...

Image Segmentation Using Deep Learning: A Survey

Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, amon...

A review on deep learning in medical image analysis

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is ef...

Deep Learning in Medical Ultrasound Analysis: A Review

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variabil...

Machine and deep learning methods for radiomics

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...

Explainable and reliable kidney CT image classification using self-supervised DeiT-Tiny transformer

IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...

Deep Learning in Mammography

OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mam...

A survey on deep learning in medical image analysis

Deep Learning in Medical Image Analysis

Deep learning for cellular image analysis

The rise of deep learning in drug discovery

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...