Artificial Intelligence Based Disease Detection In Medical Images

Artificial intelligence market in the field of healthcare is growing quite rapidly worldwide. It is helping doctors in diagnosis and predicting the future of patient health. It will help us apply machine learning to concrete problems in medicine. Our project "Artificial Intelligence Based Disease De

Project Title

Artificial Intelligence Based Disease Detection In Medical Images

Project Area of Specialization

Artificial Intelligence

Project Summary

Artificial intelligence market in the field of healthcare is growing quite rapidly worldwide. It is helping doctors in diagnosis and predicting the future of patient health. It will help us apply machine learning to concrete problems in medicine. Our project "Artificial Intelligence Based Disease Detection in Medical Images" works on the principle of neural networks for the detection of diseased and non-diseased X-RAYS. Neural networks are machine learning models, also called parallel distributed processing systems, with multiple layers between input and output. These types of machine learning models mimics the way humans learn.  Different types of neural networks are popularly used today, whereas, in our project, CNN is used for the classification of medical images.

Medical image analysis has become an active and broad area of research due to its high clinical impact in recent decades. As you know that manual analysis of X-ray images is a tiring task and requires high professional skills. It also consumes a lot of time with chances of human error. Also the radiologists are overburden that causes a delay in disease diagnosis. So, there should be a system that automatically extracts information from medical images and requires no expert recognition. It also saves time and provides high accuracy results. Different algorithms have been developed for the classification of X-rays but there is still room to get even better results. 

The main goal is to assist and facilitate the radiologists by early detection of diseases, and to achieve high diagnostic accuracy. The work is divided into two phases. Phase 1 is the training and testing of an online dataset using GPU server. Phase 2 is the training and testing of local dataset using a mobile APP.

Project Objectives

The main goal of the project is to assist and facilitate radiologists by classifying medical images

It also helps in;

Achieving high diagnostic accuracy

High sensitivity rate

Early detection of diseases

Reducing chances of human error 

Saves time

Project Implementation Method

The project implementation method includes two phases

Phase 1: Training Algorithms using online data sets                       

Hardware: GPU servers/Google colab                           

Software : Anaconda / Jupyter

Phase 2: Training local data set                  

                Mobile app development  

Benefits of the Project

Assist and facilitate the radiologist

Less human error

Rapid diagnosis and immediate detection

Reduce reading time

Technical Details of Final Deliverable

The final deliverables include a network or a system that is used for classification of medical images. It also includes a mobile APP, The mobile APP takes pictures, sends them to the network that is placed on cloud, and as a result it gives the predicted output.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Medical

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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