AI in healthcare - Labellerr

AI in healthcare

A collection of 17 posts

Enhancing Wound Image Segmentation with Labellerr

AI in healthcare

Enhancing Wound Image Segmentation with Labellerr

Wound segmentation using computer vision technology has emerged as a valuable tool in medical imaging for precise diagnosis and treatment planning. Accurate segmentation of wounds from medical images, such as photographs or scans, enables healthcare professionals to assess the size, shape, and severity of wounds with greater precision. This information

Speed Up Medical Imaging Annotation With Labellerr

Speed Up Medical Imaging Annotation With Labellerr

Table Of Content * Introduction * Challenge * Solution Using Labellerr * Conclusion * Frequently Asked Questions Introduction In healthcare industry, the accurate interpretation of medical imaging data, such as CT scans, is important for effective diagnosis and treatment planning. By using advanced algorithms and machine learning techniques, computer vision and AI enable healthcare providers

Save Cost For Cell Segmentation Annotation with Labellerr

Save Cost For Cell Segmentation Annotation with Labellerr

Introduction In medical research, particularly in the study of neurological disorders like Alzheimer's disease and brain tumors, the ability to accurately segment individual neuronal cells from microscopic images is important for understanding disease progression and evaluating the treatment. Traditional methods of manual cell segmentation are labor-intensive and time-consuming, hindering the

Generate Super Fast Annotations For Virtual Surveys Model

Generate Super Fast Annotations For Virtual Surveys Model

Introduction In the world of the home services industry, efficiency and precision are important . Property inspections are essential for moving and home insurance companies, traditionally demanded significant resources, primarily in the form of physical visits to customer homes for estimations and measurements. Imagine you're part of a home insurance or

Automate Dental Image Annotation With Labellerr

AI in healthcare

Automate Dental Image Annotation With Labellerr

Introduction Dental prediction and detection with the help of computer vision technology represent groundbreaking advancements in oral healthcare. Using algorithms and machine learning models, these systems can analyze dental images to predict potential issues and detect abnormalities with remarkable accuracy. However, the success of such systems heavily relies on the

Accelerate Tumor & Lesion Annotation With Labellerr

AI in healthcare

Accelerate Tumor & Lesion Annotation With Labellerr

Introduction Identifying tumors and lesions is a critical task in medical diagnosis and treatment planning. The advancements in machine learning and medical imaging technology, automated systems can assist healthcare professionals in accurately detecting and diagnosing tumors and lesions from medical images such as MRI scans, and CT scans. However, training

Innovative Care: Precision Medicine Empowered by Computer Vision

Computer Vision in Precision Medicine

Innovative Care: Precision Medicine Empowered by Computer Vision

Introduction Meet Sarah, a 42-year-old mother of two who is battling breast cancer. It's a difficult period for her, filled with anxiety about what comes next. Sarah has been informed she requires therapy, but the options are confusing. She is only one of millions of women diagnosed with cancer each

Beginner's Guide To Build NLP Models Using Medical Text Transcription

Beginner's Guide To Build NLP Models Using Medical Text Transcription

Introduction In the realm of healthcare, the vast amount of medical data has motivated the need for efficient methods to organize, interpret, and extract valuable insights. These data, or transcripts, document patient encounters, diagnoses, procedures, and treatment plans. Efficiently processing and organizing this information can be challenging due to its

Liver Disease Prediction Using Machine Learning

Liver Disease Prediction Using Machine Learning

Introduction Liver diseases affect millions globally, posing a significant burden on healthcare systems and individual lives. Liver diseases consists a spectrum of conditions ranging from fatty liver disease to cirrhosis, posing significant challenges to healthcare systems worldwide. Traditional methods of diagnosing liver diseases often rely on different procedures, such as

Chronic Kidney Disease Detection Using Machine Learning

Chronic Kidney Disease Detection Using Machine Learning

Introduction Kidney disease is a critical health issue affecting millions of individuals worldwide. The kidneys play a vital role in filtering waste and excess fluids from the blood, regulating blood pressure, and maintaining electrolyte balance. However, various factors, such as hypertension and diabetes, can lead to chronic kidney disease (CKD)

Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

Introduction Heart disease is the leading cause of death globally, claiming millions of lives each year, presenting a significant challenge to hospitals and doctors. Early detection and intervention are crucial in preventing and managing this condition. In recent years, machine learning techniques have emerged as a powerful tool in the

Transforming Clinical Trials: The Promise of Computer Vision

Clinical Trials

Transforming Clinical Trials: The Promise of Computer Vision

Introduction In the healthcare industry, the path from medication discovery to market approval is full of difficulties, hurdles, and uncertainty. Clinical development, the most crucial phase of this journey, is distinguished by long delays, shocking costs, and high turnover rates. According to recent studies, the challenging road to drug approval

Revolutionizing Healthcare with Computer Vision in Electronic Health Records

AI in healthcare

How Computer Vision Revolutionizing Electronic Health Records For Healthcare

Introduction Sarah, 42, has suffered from constant migraines for years, which cause throbbing pain and debilitating nausea. Despite countless doctor visits and doses of medication, relief seemed unattainable. This condition of her affected her carrer,family etc. According to the Migraine Research Foundation, approximately 12% of people in the United

Computer Vision in Telemedicine for Enhanced Healthcare Delivery

Computer Vision in Telemedicine for Enhanced Healthcare Delivery

Introduction Mr. Rahman and his daughter Aisha live in a rural village. Mr. Rahman, who suffers from hypertension and his condition needs frequent monitoring and medication changes. There being no nearby hospital, and every journey to the city mean missing a day of work and paying fees they couldn't afford.

Detecting Alzheimer's Disease with Deep Learning

Alzheimer Detection

Detecting Alzheimer's Disease with Deep Learning: A Step-by-Step Guide

Introduction The emergence of artificial intelligence (AI) and machine learning (ML) technologies in the field of healthcare has heralded promising advancements in Alzheimer's disease detection. This groundbreaking dataset on Alzheimer's comprises meticulously collected MRI images categorized into four distinct classes, delineating various stages of the disease. It aims to catalyze

Detecting Skin Cancer Using Convolutional Neural Networks

Skin Cancer Detection Model Using CNN: A Comprehensive Guide

Introduction Skin cancer is a widespread and potentially life-threatening condition affecting millions worldwide. Timely detection significantly increases survival rates, and in this digital age, Convolutional Neural Networks (CNNs) have emerged as powerful allies in automating this process. This blog serves as a detailed roadmap for ML Experts, ML Beginners and

A Detailed Guide- Parkinson's Disease Identification Model

A Detailed Guide- Parkinson's Disease Identification Model

Introduction Early detection of Parkinson's disease, a neuro degenerative condition, presents major obstacles. Biomedical measurements connected to voice provide a possible non-invasive diagnostic approach. In this work, we use a Support Vector Machine (SVM) model—which is well-known for its effectiveness in classifying large, complicated datasets—to identify patterns in