Imaginghub

Documentation

**Project Description**-Reflekt aims to use simple  camera will take photos of a user’s body every day, and algorithms will cull and crop the photos to create a profile for each mole. “So for each mole, there will be a historical record that shows what it looks like, the estimated size changes over time, discolorations, changes at the edges. Reflect could provide the doctor with a kind of dashboard that displays significant changes. “It might say that out of 15 moles on patient A’s body, two of them look like they have higher than that patient’s average mole growth in the last six months. This would give doctors more information and context than they get now with only annual or biennial visits.This would be like detecting early stage skin cancer.
With approximate 7% rise in skin cancer , this methodology would be a cost effective, time saving using a camera.

**Methodology** The  setup would  consists of a two-way mirror with a display monitor behind it, with the reflective ratio of the mirror allowing the user to see themselves as well as what’s on-screen behind the glass. The setup will also be equipped with a Raspberry Pi, a computer the size of a credit card on which pylon application will be built
**How to build pylon applications on Raspberry Pi**

The idea is that Reflekt will take photos of a user’s body every day, and algorithms will cull and crop the photos to create a profile for each mole. “So for each mole, there will be a historical record that shows what it looks like, the estimated size changes over time, discolorations, changes at the edges
Will have to simulate camera via machine learning to cover every fraction of human body via hands wide side ways and legs opened up to shoulder width pose.

With reference being screen behind mirror in which  visible  image (this will coincident with image formed by camera's on the screen)it would be accompanied by  a green signal  and buzzer(to acknowledge that successful image has been taken) for cameras to have every human body part (frontal , side , back)in focus.Once frontal part is fine , the sides and back will simply be turning around the same point.

**Challenges**- 1)For quick scan multiple cameras will be required and the data can be clubbed up for use.
                             2)Nude images need to be taken care off by coding protection.
                             3)Parts like inner thigh, arm pits imaging would be a challenge
                             4) 3D scan to detect moles frontal , side and back.
One of the biggest challenges is to get enough of the right kind of data to train their models to identify moles accurately.

Project Description-Reflekt aims to use simple camera will take photos of a user’s body every day, and algorithms will cull and crop the photos to create a profile for each mole. “So for each mole, there will be a historical record that shows what it looks like, the estimated size changes over time, discolorations, changes at the edges. Reflect could provide the doctor with a kind of dashboard that displays significant changes. “It might say that out of 15 moles on patient A’s body, two of them look like they have higher than that patient’s average mole growth in the last six months. This would give doctors more information and context than they get now with only annual or biennial visits.This would be like detecting early stage skin cancer. With approximate 7% rise in skin cancer , this methodology would be a cost effective, time saving using a camera.

Methodology The setup would consists of a two-way mirror with a display monitor behind it, with the reflective ratio of the mirror allowing the user to see themselves as well as what’s on-screen behind the glass. The setup will also be equipped with a Raspberry Pi, a computer the size of a credit card on which pylon application will be built How to build pylon applications on Raspberry Pi

The idea is that Reflekt will take photos of a user’s body every day, and algorithms will cull and crop the photos to create a profile for each mole. “So for each mole, there will be a historical record that shows what it looks like, the estimated size changes over time, discolorations, changes at the edges Will have to simulate camera via machine learning to cover every fraction of human body via hands wide side ways and legs opened up to shoulder width pose.

With reference being screen behind mirror in which visible image (this will coincident with image formed by camera's on the screen)it would be accompanied by a green signal and buzzer(to acknowledge that successful image has been taken) for cameras to have every human body part (frontal , side , back)in focus.Once frontal part is fine , the sides and back will simply be turning around the same point.

Challenges- 1)For quick scan multiple cameras will be required and the data can be clubbed up for use. 2)Nude images need to be taken care off by coding protection. 3)Parts like inner thigh, arm pits imaging would be a challenge 4) 3D scan to detect moles frontal , side and back. One of the biggest challenges is to get enough of the right kind of data to train their models to identify moles accurately.

Code

GitHub Repository

https://github.com/egeozsoy/Mole_cancer_detection

Commits

egeozsoy	pushed 96f5ea5f751bed65b0d63c154228b00d42c3d53b	- adjusted params so it overfits, can be controlled with dropout	2018-09-16 08:53:41 UTC
egeozsoy	pushed 6d6c79ac8d3272b5925f7af5dbde5ad11f7b094e	- this worked on cats vs dogs!	2018-09-01 11:55:58 UTC
egeozsoy	pushed 5a7da1416030a1fb59ac40b6c8656a949868e81b	- network changes	2018-08-28 05:56:52 UTC
egeozsoy	pushed 6f1f48749fc7e25b580751962f60f29d21c3737a	- network changes	2018-08-27 22:27:06 UTC
egeozsoy	pushed 11b249b7871ff8392e082ab4d8368f890ef990aa	- label image and retrain are tensorflow code	2018-08-27 12:48:31 UTC
egeozsoy	pushed 9264c574df0cba282987f183d236dc37e8dcd888	- resetted create_data	2018-08-26 19:03:24 UTC
egeozsoy	pushed 3480a87996a688beea64d81e65e6b603ef9f5fde	Initial commit	2018-08-25 19:34:25 UTC

Schematics

Title	Description	Format
Mole scan	Way of scanning moles	jpg

Bill of materials

Name	Quantity	Unit Price
Rasberry Pi	1
UP embedded vision starter kit	1	349.00 $
	Total 349.00 $

Info

Project State

Public Project

Licences

Software Licence: Project has no software

Hardware Licence: Project has no hardware

Admins

sharad-garg

Members

au_chrismor

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Comments

Hi! Reflekt sounds to get a good healthy vision support for doctors. What about other parts of body? Behind ears, belly, back, all sides of legs and so on? Did you see the other health-related projects here? Maybe you can come together and support each other. If you want, you can start a discussion in our forum.

Hi , Thanks for the advice.Actually I have been sorting out the things..Every part needs to be focused , so the idea is to have an algorithm to analyse every part where in person is required to change positions like face recognition works...first recognition will be challenge. Also data protection is to be considered as naked images need to somehow coded .

You're welcome! Of course, data protection will be a huge topic, but maybe it's possible to integrate it into a kind of voluntarily self-service of a doctor's app or website. I guess people are more willing to use it if e.g. skin cancer is a topic in a family's history. Are you already working on the algorithm and recognition? What are you planning to do next?

Hello. I am an instrumentation engineer who is bad at coding and looking for my Steve Wozniak...I know what and how to be done in terms of logics.

Use our forum and start a discussion. This will be the right place to ask for participants and coding support. Don't forget to link to your project.

I have done some work in this space in the past, but the current range of cameras and SBC's make it much more practical. Count me in!

Thanks. let’s reflekt

Hi,please go through my idea and let me know if you can code it. I have to do it within 4 days for submission to contest second phase of smart life contest. I saw your profile and I am sure you can do it.

Reflekt