Computer vision has significant potential to improve healthcare by enabling more accurate medical image analysis, detecting conditions like tumors faster, and assisting with surgical guidance. It can analyze images like x-rays and scans to extract information not visible to the human eye. However, challenges remain around data limitations, lack of transparency in algorithms, and ensuring patient privacy and data security. Overall, computer vision offers possibilities to advance medical research, improve diagnoses, and enhance the patient experience.

1. MANIPAL INSTITUE OF TECHNOLOGY , MANIPAL MASTER OF COMPUTER APPLICATION
( M C A)
DEPARTMENT OF DATA SCIENCE AND COMPUTER APPLICATIONS
Computer Vision in Health Care
Deeksha U 220970047
Aishwarya Kulal 220970036

2. CONTENTS
 Objectives
 Introduction to Computer Vision
 Computer Vision system methods
 Applications
 Conclusion

3. OBJECTIVE
 To highlight the advantages of Computer Vision in detecting and
diagnosing medical conditions.
 To explore emerging trends and future developments in computer vision
for healthcare.
 To highlight the impact of computer vision on improving patient
outcomes.
 To discuss ethical considerations and potential concerns.

4. WHATIS COMPUTER VISION?
 computer vision is a subfield of artificial intelligence that equips computers with the extraordinary ability
to analyze, interpret, and derive meaning from visual data, such as images and videos. This technology
allows machines to see and understand the world in ways that were once thought to be exclusive to
human perception.
 The image data can take many forms, such as video sequence, depth images, views from multiple
cameras, or multi dimensional data from a medical scanner.
Figure:1depth image (source: Turning.com) Figure:2 camera image( source: Xenonstack ) Figure:3 medical scnner(source:vision)

5. COMPUTER VISION SYSTEM METHODS
Image acquisition
In the image acquisition step, the incoming light wave from an object is converted into an electrical signal by a combination of
photo-sensitive sensors. These small subsystems fulfill the role of providing your machine vision algorithms with an accurate
description of the object.
Recognition an interpretation
Recognition is about identifying objects, patterns, or features in visual data, while interpretation involves understanding
the context and making inferences based on recognized elements.
enable systems to both recognize what's in an image or video and understand the meaning and context behind it, making
computer vision valuable for a wide range of applications, from autonomous vehicles to healthcare and surveillance.
Segmentation
Image segmentation is like dividing a picture into different parts based on what each part looks like. The goal is to find
objects or boundaries in the picture. This process makes the image simpler and easier to analyze effectively. Segmentation is
the technique of partitioning an image into different regions, guided by the properties of pixels. This process aims to identify
objects or boundaries within the image for the purpose of simplifying it and facilitating more effective analysis.

6. WHAT DOES COMPUTERVISIONMEAN FOR THE HEALTHCARESECTOR?
 Computer vision is used in the healthcare sector to improve medical treatments and procedures, accelerate
healthcare research and improve the overall patient experience. It also enables healthcare professionals to
make better decisions about patient treatment.
 In the healthcare sector, this AI-based technology is used with high-tech cameras and sensors to visualize
and extract information that can be used for a variety of applications in healthcare management, treatment,
and research.

7. EXPLORINGCOMPUTER VISION’S IMPACT ON HEALTHCARE
Betterimageanalysis
Computer vision has the ability to recognize patterns and make diagnoses in medical images
with much higher accuracy and speed and fewer errors. It has the potential to extract
information from medical images which are not visible to the human eye.
Figure4:image analysis(source:v7labs)

8. DETECTING TUMORS AND CANCERS
Computer vision systems can help detect brain tumors with higher speed and accuracy. Additionally, computer vision
systems can also be trained through ML and deep learning with data of cancerous and healthy tissues to more
accurately detecting skin and breast cancer.
Figure:5 Tumor(source: encod.com)

9. BETTER HEALTHCARE RESEARCH
Computer vision is revolutionizing medical research and drug development by
automating tasks like cell counting, speeding up drug discovery, and providing valuable
insights into cellular behavior, significantly advancing the search for new treatments.
1.Cell Counting:Computer vision accelerates research by automating cell counting in microscopic
images, ensuring speed and accuracy compared to manual methods.
2. Drug Discovery:It expedites drug discovery by automating the analysis of compounds' effects
on cell growth, streamlining the identification of potential drug candidates.
3. Phenotypic Analysis: Computer vision helps understand how treatments affect cell behavior
and characteristics, aiding in the development of new therapies.
4. Data Management and Quality Control: It efficiently manages vast image data, standardizes
protocols, and improves the quality and reproducibility of research findings, contributing to safer
and more effective treatments

10. REDUCED PATIENT MIXUP
Patient misidentification is a common issue in the healthcare sector. This can lead to dangerous
consequences for the patient and the healthcare provider. A computer vision-enabled face
recognition system can eliminate this problem.

11. INCREASED WORKPLACE SAFETY
Surveillance systems enabled through computer vision and AI can monitor the staff for potential incidents and
alert relevant authorities when needed. They can also track if the staff uses appropriate safety equipment and
procedures.
Figure: 6 Safety equipment( source:visco.ai)

12. SURGICAL GUIDANCE
Computer vision technology is also used to guide surgeons during procedures by using cameras enabled by
machine vision.
Figure:7 surgical guidance( source :itnonline.com)

13. CHALLENGES AND CONCERNS IN IMPLEMENTING COMPUTER VISION IN HEALTHCARE
1. Lack of Transparency: Many AI algorithms used in
healthcare are like black boxes, making it hard to
understand how they arrive at their decisions. This
can hinder trust among healthcare professionals and
raise regulatory concerns.
2. Data Limitations :AI algorithms require large
datasets for training, but there's often a shortage of
high-quality data, especially for rare diseases. This
can affect the accuracy of diagnoses, especially in rare
cases, which can deter doctors from using these tools
3. Data Privacy: Protecting patient data is a major
challenge. Data breaches and commercial use of
patient information can erode trust in digital
healthcare solutions. The healthcare sector is also a
prime target for cyberattacks, highlighting the need
for improved data security.

14. CONCLUSION

15. BIBLIOGRAPHY