Real Time-based Melanoma Detection Using Image Processing and Machine Learning

After reading multiple research paper I understand a dangerous cancers skin cancer melanoma which people are facing in their daily life. But treatment of this cancer is so expensive every not afford this treatment. So we make Real-time detection of melanoma by using machine learning and cnn algorith

Project Title

Real Time-based Melanoma Detection Using Image Processing and Machine Learning

Project Area of Specialization

Computer Science

Project Summary

After reading multiple research paper I understand a dangerous cancers skin cancer melanoma which people are facing in their daily life. But treatment of this cancer is so expensive every not afford this treatment. So we make Real-time detection of melanoma by using machine learning and cnn algorithms. The project objective is to provide inexpensive dermoscope and Detect melanoma machine learning that people could use as a substitute for the expensive one’s which not everybody can afford. After a lot of research and learning, we have come up with many hardware components and machine learning model that can be used for making a cheap dermoscope such as Raspberry Pi 4, Camera Module, Laptop, power supply. Connecting all these hardware components together, Camera Module connected with Raspberry pi. Camera connected by ribbon cable. For capturing real-time images. And Raspberry Pi is connected with laptop by using HDMI cable to show disease report. We use Convolutional neutral network Algorithms and Python coding and some libraries for machine learning such as pandas, keras, numpy tensor flow, opencv. Opencv is provide UI.

Project Objectives

The fundamental objective of our project is Design a “Dermoscope” prototype which is low-cost device and machine learning model trained. The international sellers of dermoscope such as Dermlite or Vidix are the international brands due to which most of their dermoscope products have to be shipped online through shopping websites. Because of this process dermoscope is so expensive day by day. Our aim is not only to develop a low-cost dermoscope for Real Time Melanoma Detection using the Machine Learning model, that is available for everybody to access it.

Project Implementation Method

We have two parts for working one is software Design and seconds is hardware design. Firstly, we talk about software design so we have two things train machine learning model and this model connect opencv user interface where real-time detection is working. Now, we talk about hardware Design so we collect some hardware require component and connecting together and check it is operational or not. we use camera module because camera is common is device in our peoples. Everybody know that how to use camera. Our camera is connecting with raspberry pi It helps real-time detection. Camera connected Raspberry Pi by using ribbon cable. and Raspberry pi 4 connect with LCD by using HDMI where it shows result. we are going to test our device to see if it is detecting or not. Firstly, check camera focusing on lesions. Make some physical changes firstly raspberry set into Pi case with cooling fan. It refers to maintaining the device and to fix any bugs or issues.

Benefits of the Project

The main aim to create this project is make availability of our model in common people Our project has some benefits which is define below: it is helpful for every person.it is user-friendly and Humans require some rest but our model works 24/7 need power to operate. It is also of source of saving live. Melanoma skin cancer require early detection of cancer, so our model diagnose skin cancer with efficient accuracy.

Technical Details of Final Deliverable

Our project is based on machine learning and cnn algorithms. The aim of our project is provide low-cost dermoscope prototype and machine learning model so we have some hardware detail and software detail so we collect some hardware require component and connecting together and check it is operational or not. We use camera module. Our camera is connecting with raspberry pi 4. It helps real-time detection. Camera connected Raspberry Pi by using ribbon cable Raspberry pi 4 connect with LCD by using HDMI where it shows output.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Health

Other Industries

Medical

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com