Academic Articles & Research Papers

Deep Learning: A Primer for Radiologists

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...

Application of visual transformer in renal image analysis

Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with th...

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effect...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery

Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several ...

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Abstract Background Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of...

Deep Learning-Based Approaches for Brain Tumour Segmentation and Classification

Brain tumours are caused by the abnormal growth of cells in the brain. This occurs mainly due to genetic changes or exposure to X-ray radiation. When the tumours are detected early, they can be removed via surgery. The tumour can be removed...

Machine learning in breast MRI

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...

Hybrid feature optimized CNN for rice crop disease prediction

Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy cr...

Causes selection and risk level prediction of coal mine gas explosion accident

In order to accurately predict the risk level of coal mine gas explosion accidents, based on the feature vector in line with the actual situation, the particle swarm optimization probabilistic neural network (RWPSO-PNN) is improved to reali...

Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

The spread of the coronavirus has claimed the lives of millions around the world, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of th...

Analysis of applying machine learning methods in rock classification problems on core samples

Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely importan...

Diversity in Machine Learning

Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine...

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted ...

BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations.

A Proposed Arabic Handwritten Text Normalization Method

Text normalization is an important technique in document image analysis and recognition. It consists of many preprocessing stages, which include slope correction, text padding, skew correction, and straight the writing line. In this side, t...

Deep learning-based segmentation and subtype prediction of renal cell carcinoma on contrast-enhanced CT.

A Bio-Inspired Antigen-Antibody Optimization Approach for MRI-Based Stroke Lesion Segmentation.

SEMANTIC URBAN MESH SEGMENTATION BASED ON AERIAL OBLIQUE IMAGES AND POINT CLOUDS USING DEEP LEARNING

The use of deep machine learning methods for semantic classification of city mesh models is one of the current trends in geoscience development. Thanks to the thriving development of Convolutional Neural Networks (CNNs) it is now achievable...

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to...

Applications of Artificial Intelligence in Dentomaxillofacial Diagnostics

Introduction: The introduction of artificial intelligence-driven applications is revolutionizing dentomaxillofacial imaging. Objectives: To describe the current status of artificial intelligence applications in dentomaxillofacial diagnosti...