Patients should be able to trust their doctors blindly. However, for most of the masses getting a second opinion is has become a necessity. A doctor, albeit a human, should be able to guarantee their diagnoses of the patient. With the current advancements of technology, it is shocking that technology is not being utilized to give this guarantee. Technology is present and ever evolving to remove human error. Thus, with the aim of using new technologies such as deep learning and neural networks, our team is developing a way to assist doctors in their diagnoses by analysing chest x-rays. Chest x-rays show a number of diseases including heart and lung issues, fluid vessels and airways. Our software based solution, will be able to analyse chest x-rays and mark any abnormalities found thereby pointing towards the correct diagnoses and improving a doctors’ efficiency in diagnoses.

1. A deep learning solution to
diagnose diseases in chest x-rays

2. TEAM MEMBERS
Tariq Aijaz Sakina Petiwala Talha Khatri
2

3. SUPERVISOR
DR. MOHAMMAD SARIM
▪ PhD (Computer Vision and Machine
Learning), University of Surrey Guildford,
Surrey, United Kingdom – 2010
▪ MSc (Physics with major in Electronics),
University of Karachi – 2000
▪ BSc (Hons) (Physics), University of Karachi
- 1999
3

4. 1.
THE PROBLEM
Motivation for this project
4

5. 1 in 20 patients
are misdiagnosed when they first
go to a doctor.
5

6. ”
The main reasons for misdiagnoses
have proved to be a lack of expertise,
lack of exposure and the enormous
workload faced by doctors
6

7. HOW VIABLE DO YOU THINK A PRODUCT THAT HELPS A
DOCTOR DIAGNOSE A PATIENT IS?7

8. 2.
THE SOLUTION
Our solution to the problem of misdiagnoses
8

9. DEEP
LEARNING
Teach computers to diagnose a
patient’s x-ray to assist a
doctor’s diagnoses.
9

10. ADVANTAGES
▹ Faster diagnoses
▹ Accurate diagnoses
▹ Less pressure on the doctor
This technology is supposed to help a
doctor make more accurate diagnosis,
faster, not replace a doctor.
10

11. WE HELP
DOCTORS
MAKE
ACCURATE
DECISIONS,
FASTER.
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12. 3.
THE DEMO
A working prototype of our solution
12

13. Place your screenshothere
13 WELCOME
On the landing page you
see a simple button to
begin the process along
with a navigation menu
which has instructions.

14. 14
X-RAY PREPROCESSING
SCREEN
Upon uploading an x-ray
you get an option to
preprocess the x-ray
which can give more
accurate results. The
preprocessing options
include brightness and
contrast.

15. 15 DIAGNOSES SCREEN
Once the x-ray is
submitted, the machine
runs and results in a
diagnoses on screen
which highlights any
abnormalities.

16. 16 VIEW SIDE BY SIDE
There is also an option of
viewing the two x-rays
side by side. This can give
more information to the
doctor on the highlighted
abnormality.

17. 4.
THE TECHNOLOGIES
All the technologies used in the project
17

18. ANGULAR 4
The frontend has been
made with Angular 4, a
material design library for
fast prototypes.
18

19. PYTHONAn interpreted high-level
programming language for
general-purpose programming
PYTORCHPyTorch is an open source
Python library, based on Torch
for machine learning and deep learning
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20. 5.
THE DATA
Information about the data used
20

21. 21
NIH DATA
The NIH Clinical Center Data Set:
▹ Publicly available
▹ Labelled

22. No proof of correct
labels
The data has been
labeled with an NLP
algorithm that mines
radiological reports and
labels the images. No
availability of the
reports to check labels.
PROBLEMS WITH THE DATA
Size of data
For a deep learning
machine to work, we
needed a very large
amount of data. This
dataset only contains
121,000 images.
22

23. PROCESSING ON THE DATA23
Check the
images. Do a
sanity check
Check image labels
by matching images
to ideal images.
Data augmentation
to increase amount
of data.

24. 6.
THE CONCLUSION
Finally.
24

25. 25
60% of Pakistanis
have either been
misdiagnosed or
know someone who
has been
misdiagnosed

26. 26
THANKS!
This problem has
an easy solution:
▹ Use technology
▹ Take burden off doctors