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

In the United States, around 2.5% of the population suffers from chronic wounds that affect their quality of life and managing wounds has a major financial impact on health care. Wound segmentation using computer vision technology has emerged as a valuable tool in medical imaging for precise diagnosis and

Generate Super Fast Annotations For Virtual Surveys Model

Generate Super Fast Annotations For Virtual Surveys Model Using SAM

Table of Contents 1. Introduction 2. Challenge 3. Solution: Labellerr’s Role in Virtual Surveys 4. Conclusion 5. Frequently Asked Questions 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 demanding significant resources, primarily

Automate Dental Image Annotation With Labellerr

AI in healthcare

Automate Dental Image Annotation With Labellerr Using SAM

Table of Contents 1. Introduction 2. Challenge 3. Solution: Labellerr for Annotating Dental Imaging 4. Conclusion 5. Frequently Asked Questions 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

Speed Up Medical Imaging Annotation With Labellerr

Speed Up Medical Imaging Annotation With Labellerr

Table of Contents 1. Introduction 2. Challenge 3. Solution: Labellerr's Role in CT Scan Analysis 4. Conclusion 5. 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

Save Cost For Cell Segmentation Annotation with Labellerr Using SAM

Save Cost For Cell Segmentation Annotation with Labellerr Using SAM

Table of Contents 1. Introduction 2. Challenge 3. Solution: Labellerr's Role in Cell Segmentation 4. Conclusion 5. Frequently Asked Questions 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

Accelerate Tumor & Lesion Annotation With Labellerr

AI in healthcare

Accelerate Tumor & Lesion Annotation With Labellerr

Table of Contents 1. Introduction 2. Challenge 3. Solution: Auto Labeling in Tumor and Lesion Detection 4. Conclusion 5. Frequently Asked Questions 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

Innovative Care: Precision Medicine Empowered by Computer Vision

Computer Vision in Precision Medicine

Innovative Care: Precision Medicine Empowered by Computer Vision

Table of Contents 1. Introduction 2. Need of Computer Vision 3. Implementing a Model 4. Challenges and Considerations 5. Conclusion 6. Frequently Asked Questions 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.

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

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

Table of Contents 1. Introduction 2. Medical Text Transcription Classification Using NLP 3. Model Building 4. Challenges of NLP in Medical Text Classification 5. Conclusion 6. Frequently Asked Questions Introduction In the realm of healthcare, the vast amount of medical data has motivated the need for efficient methods to organize,

Liver Disease Prediction Using Machine Learning

Liver Disease Prediction Using Machine Learning

Table of Contents 1. Introduction 2. Steps for Model Building 3. Conclusion 4. Frequently Asked Questions 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

Chronic Kidney Disease Detection Using Machine Learning

Chronic Kidney Disease Detection Using Machine Learning

Table of Contents 1. Introduction 2. Steps for Model Building 3. Conclusion 4. Frequently Asked Questions 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

Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

Table of Contents 1. Introduction 2. Build ML Model 3. Conclusion 4. Frequently Asked Questions 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

Transforming Clinical Trials: The Promise of Computer Vision

Clinical Trials

Transforming Clinical Trials: The Promise of Computer Vision

Table of Contents 1. Introduction 2. The Importance of Computer Vision 3. Implementation 4. Challenges 5. How Labellerr Helps 6. Conclusion 7. Frequently Asked Questions 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

Revolutionizing Healthcare with Computer Vision in Electronic Health Records

AI in healthcare

How Computer Vision Revolutionizing Electronic Health Records For Healthcare

Table of Contents 1. Introduction 2. Computer Vision in Electronic Health Record (EHR) Analysis 3. Model Building 4. How Labellerr Helps 5. Frequently Asked Questions 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,

Computer Vision in Telemedicine for Enhanced Healthcare Delivery

Computer Vision in Telemedicine for Enhanced Healthcare Delivery

Table of Contents 1. Introduction 2. Why Computer Vision? 3. Model Building 4. Challenges 5. How Labellerr Helps 6. Frequently Asked Questions 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

Detecting Alzheimer's Disease with Deep Learning

Alzheimer Detection

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

Table of Contents 1. Introduction 2. About Dataset 3. Hands-on Tutorial 4. Uses of Alzheimer's Disease Detection in Healthcare 5. Conclusion 6. Frequently Asked Questions 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.

Detecting Skin Cancer Using Convolutional Neural Networks

Skin Cancer Detection Model Using CNN: A Comprehensive Guide

Table of Contents 1. Introduction 2. About Dataset 3. Use Cases of Skin Cancer Detection Model 4. Hands-on Tutorial 5. Conclusion 6. Frequently Asked Questions 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

A Detailed Guide- Parkinson's Disease Identification Model

A Detailed Guide- Parkinson's Disease Identification Model

Table of Contents 1. Introduction 2. About Dataset 3. Hands-on Tutorial 4. Conclusion 5. Frequently Asked Questions 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