The healthcare industry has benefited a lot from the emergence of artificial intelligence (AI) solutions. One of the emerging areas of artificial intelligence today is computer vision, which may support many different applications and provide life-saving functions for patients. Computer vision is now helping more and more doctors better diagnose patients, monitor disease progression, and prescribe correct treatments. This technology can not only help medical professionals save time in daily work, but also provide patients with more time. The impact of computer vision on medical applications based on tasks such as medical image analysis, predictive analysis, or healthcare monitoring shows that the healthcare industry has many benefits. Want to know what computer vision is and how it can help with health monitoring? You are in the right place. This article will teach you all about computer vision and its potential to improve health systems. Video : https://youtu.be/AHvCDsCHVkQ

1. Pie & AI: Guna - Computer Vision
in HealthCare
By : Vishwas N

3. Then what is the role of the machine here?
That will be your question for getting the things done
in the problem statements that have no solution.(this
is stupidity and >>>)

4. I too had this question but as time moved and I started
learning I realised a lot about this field:

5. But I solved that question by thinking about
Problem Statement:

6. What is the purpose of Ai in medicine?

7. Why do we need AI in Medicine?

8. The sole purpose of AI is to make a Precise decision?
● A decision can be influence by a lot of factors

9. The sole purpose of AI is to make a Precise decision?
● A decision can be influence by a lot of factors
● A decision can be a unique solution to the problem statements that we have.

10. The sole purpose of AI is to make a Precise decision?
● A decision can be influence by a lot of factors
● A decision can be a unique solution to the problem statements that we have.
● A decision can be a collective form of the plausible cure and a probable right
treatment

11. The sole purpose of AI is to make a Precise decision?
● A decision can be influence by a lot of factors
● A decision can be a unique solution to the problem statements that we have.
● A decision can be a collective form of the plausible cure and a probable right
treatment
● A decision can be made to just ease your work in the world that has all
reasons to get the things moving(where you also need to take the
consideration of your colleagues and clients)

12. The sole purpose of AI is to make a Precise decision?
● A decision can be influence by a lot of factors
● A decision can be a unique solution to the problem statements that we have.
● A decision can be a collective form of the plausible cure and a probable right
treatment
● A decision can be made to just ease your work in the world that has all
reasons to get the things moving(where you also need to take the
consideration of your colleagues and clients)
● A decision is now we are trying to make a new set of problem statement
doable.

17. What is intelligence?
We have never experienced it but we want to build it.

18. What is intelligence?
We have never experienced it but we want to build it.
And intelligence is not the compute intelligence but it is human compatible
intelligence programmed.

19. What is a neural network?
● It is just a code to only some people.

20. What is a neural network?
● It is just a code to only some people.
● It is a very new form of the intelligence that can be programmed.

21. What is a neural network?
● It is just a code to only some people.
● It is a very new form of the intelligence that can be programmed.
● It is a new way of thinking about a solution to the problem statements.

22. Why machines
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data

23. Why machines
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data
Advantage of Machine

24. Why machines
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data
Advantage of Machine
a.Can retain information

25. Why machines
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data
Advantage of Machine
a.Can retain information
b.Becomes smarter over time

26. Why machines
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data
Advantage of Machine
a.Can retain information
b.Becomes smarter over time
c.Machine is not susceptible to Sleep deprivation, distractions, information overload and short-term
memory loss

27. Why AI for the Medical world
● Highly repetitive work

28. Why AI for the Medical world
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate

29. Why AI for the Medical world
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate
● Augment the professionals, offering them expertise and assistance.

30. Why AI for the Medical world
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate
● Augment the professionals, offering them expertise and assistance.
● Replace personnel and staffing in medical facilities, particularly in administrative
functions,

31. Why AI for the Medical world
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate
● Augment the professionals, offering them expertise and assistance.
● Replace personnel and staffing in medical facilities, particularly in administrative
functions,
● Managing wait times & automating scheduling

32. Why AI for the Medical world
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate
● Augment the professionals, offering them expertise and assistance.
● Replace personnel and staffing in medical facilities, particularly in administrative
functions,
● Managing wait times & automating scheduling
● “Deep-learning devices will not replace clinicians

33. The application of AI in medicine has two main branches:
1. Virtual branch
2. Physical branch.

34. Virtual Branch
The virtual component is represented by Machine Learning, (also called Deep Learning)-
mathematical algorithms that improve learning through experience.
Three types of machine learning algorithms:
1. Unsupervised (ability to find patterns)
2. Supervised (classification and prediction algorithms based on previous examples)
3. Reinforcement learning (use of sequences of rewards and punishments to form a
strategy for operation in a specific problem space)

35. Physical Branch
It includes:
1.Physical objects,
2.Medical devices
3.Sophisticated robots for delivery of care (carebots)/ robots for surgery.

36. Where AI is in power for the use cases:
● Highly repetitive work
● Empower doctors
○ help them deliver faster and more accurate
● Augment the professionals, offering them expertise and assistance.
● Replace personnel and staffing in medical facilities, particularly in administrative
functions,
● Managing wait times & automating scheduling
● “Deep-learning devices will not replace clinicians

37. What is the best optimized solution for the medical
application?
You have packages like :
1. Tensorflow
2. Keras
3. Optuna
4. Hypertune

38. We need to know these
● Development costs

39. We need to know these
● Development costs
● Integration issues

40. We need to know these
● Development costs
● Integration issues
○ Ethical issues

41. We need to know these
● Development costs
● Integration issues
○ Ethical issues
○ Reluctance among medical practitioners to adopt AI

42. We need to know these
● Development costs
● Integration issues
○ Ethical issues
○ Reluctance among medical practitioners to adopt AI
○ Fear of replacing humans

43. We need to know these
● Development costs
● Integration issues
○ Ethical issues
○ Reluctance among medical practitioners to adopt AI
○ Fear of replacing humans
● Data Privacy and security

44. We need to know these
● Development costs
● Integration issues
○ Ethical issues
○ Reluctance among medical practitioners to adopt AI
○ Fear of replacing humans
● Data Privacy and security
○ Mobile health applications and devices that use AI

45. We need to know these
● Development costs
● Integration issues
○ Ethical issues
○ Reluctance among medical practitioners to adopt AI
○ Fear of replacing humans
● Data Privacy and security
○ Mobile health applications and devices that use AI
○ Lack of interoperability between AI solutions

46. We need to know these
• Development costs
• Integrationissues
• Ethical issues
• Reluctance among medical practitioners to adopt AI
• Fear of replacing humans
• Data Privacy and security
• Mobilehealth applications and devices that useAI
• Lack of interoperability between AI solutions
• Data exchange
• Need for continuous training by data from clinical studies
• Incentives for sharing data on the system for further development and
improvement of the system. Nevertheless,
• All the parties in the healthcare system, the physicians, the pharmaceutical
companies and the patients, have greater incentives to compile and exchange
information

47. We need to know these
• Development costs
• Integration issues
• Ethical issues
• Reluctance among medical practitioners to adopt AI
• Fear of replacing humans
• Data Privacy and security
• Mobile health applications and devices that use AI
• Lack of interoperability between AI solutions
• Data exchange
• Need for continuous training by data from clinical studies
• Incentives for sharing data on the system for further
development and improvement of the system. Nevertheless,

48. We need to know these
• Development costs
• Integrationissues
• Ethical issues
• Reluctance among medical practitioners to adopt AI
• Fear of replacing humans
• Data Privacy and security
• Mobilehealth applications and devices that useAI
• Lack of interoperability between AI solutions
• Data exchange
• Need for continuous training by data from clinical studies
• Incentives for sharing data on the system for further development and
improvement of the system. Nevertheless,
• All the parties in the healthcare system, the physicians, the pharmaceutical
companies and the patients, have greater incentives to compile and exchange
information

49. We need to know these
• Development costs
• Integration issues
• Ethical issues
• Reluctance among medical practitioners to adopt AI
• Fear of replacing humans
• Data Privacy and security
• Mobile health applications and devices that use AI
• Lack of interoperability between AI solutions
• Data exchange
• Need for continuous training by data from clinical studies
• Incentives for sharing data on the system for further development and improvement of the system. Nevertheless,
• All the parties in the healthcare system, the physicians, the pharmaceutical companies and the patients, have greater incentives to compile and exchange information
• State and federal regulations

50. We need to know these
• Development costs
• Integration issues
• Ethical issues
• Reluctance among medical practitioners to adopt AI
• Fear of replacing humans
• Data Privacy and security
• Mobile health applications and devices that use AI
• Lack of interoperability between AI solutions
• Data exchange
• Need for continuous training by data from clinical studies
• Incentives for sharing data on the system for further development and improvement of the system. Nevertheless,
• All the parties in the healthcare system, the physicians, the pharmaceutical companies and the patients, have greater incentives to compile and exchange information
• State and federal regulations
• Rapid and iterative process of software updates commonly used to improve
existing products and services

52. Convolution Formula

55. An architecture

56. Components of a Neural Network
● Activation Function.

57. Components of a Neural Network
● Activation Function.
● Optimised hyperparameters.

58. Components of a Neural Network
● Activation Function.
● Optimised hyperparameters.
● Data,DATA,data

60. Units have flavours

61. UNET is the magic pill
U-Net is more competitive than traditional versions, in terms of design and pixel-
based image segmentation generated by convolutional neural network layers.
Even with small dataset photos, it's successful. This architecture was first
presented by the study of biomedical images.

62. There are other
● Variational Auto Encode

63. There are other
● Variational Auto Encode
● AutoEncoder and many more

64. So now let's jump to data

65. And the code