preDoct A disease diagnostic error reduction and data transformation machine

preDoct is designed to reduce diagnostic errors in medical diseases. It detects early stage diseases and predicts stage of the disease so that the dicision of the physician can be strengthened and the related human error can be minimized. It is designed using state of the art, Machine Le

Project Title

preDoct A disease diagnostic error reduction and data transformation machine

Project Area of Specialization

Artificial Intelligence

Project Summary

preDoct is designed to reduce diagnostic errors in medical diseases. It detects early stage diseases and predicts stage of the disease so that the dicision of the physician can be strengthened and the related human error can be minimized.

It is designed using state of the art, Machine Learning, Machine Vision, AI and Big Data technologies.

Project Objectives

The objective of preDoct is to aid physicians, radiologists, pathologists or surgeons in determining the ocuurence, nature and stage of such abnormal growths in human body that may cause harm to humans.

It is trained on very large datasets of different orientation and from different areas of the world and thus it detects and predicts with a lot higher accuracy.

The aim is to revolutionize the methods of disease diagnosis and have better, accurate more precised decisions so that the loss of human lives or unnecessary or unconcerned treatments can be reduced.

Project Implementation Method

The implementation method of the project can be divided as:

• Disease Selection:  A single disease should be selected for testing the complete project's concept. Once a model is created to predict results for a certain disease the it can be replicated to predict results of various other diseases also. We have selected Lung Cancer for this purpose since it is the most fatal disease in the world.
• Data Collection: A huge amount of data is required to train and test the model so that it may predict with a greater accuracy.
• Data Preprocessing: Preprocessing of data is required to remove the unwanted and make the wanted data clean so that the model can utilize it in the best possible way and generate the best results.
• Making CNN: A convolutional Neural Network(CNN) is built to classify different results of Lung Cancer. The CNN is based on Deep Learning method, in AI.
• Improving CNN Accuracy: The accuracy of CNN is improved by applying various useful techniques such as, Transfer Learning, Hyperparameters tuning, etc.
• Making UI/UX For Product Usage Interface: A user interface is created to assist professionals in the use of preDoct.

Benefits of the Project

Following are the benefits preDoct would deliver:

• Reduce Diagnostic errors in medical diseases.
• Strengthen Professionals' decisions.
• Save Doctor's Time.
• Serve as initial doctor in remote areas.
• Assist rural life regarding healthcare.

Technical Details of Final Deliverable

The final product will comprise of the following units:

1. A scanner system to take the X-Rays, CT scans or other data of patient.
2. A UI for doctor where the predicted results of the disease a specific patient has, will be displayed. It will also display the previous medical record of the patient.
3. A UI for doctor's assistant where complete medical history of patient will be recorded and saved in a database for further orfuture use. 

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Final Deliverable of the Project

HW/SW integrated system

Core Industry

Medical

Other Industries

IT , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT), Big Data

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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