Iris Based Lungs Disorder Diagnosis System

The project is based on iridology, According to the iridologists each iris(left or right) shows the malfunctioning of the corresponding lung by patterns or marks.Human lungs are essential respiratory organs. Different Obstructive Lung Diseases (OLD) such as bronchitis, asthma, lungs cancer etc.

Project Title

Iris Based Lungs Disorder Diagnosis System

Project Area of Specialization

Artificial Intelligence

Project Summary

The project is based on iridology, According to the iridologists each iris(left or right) shows the malfunctioning of the corresponding lung by patterns or marks.Human lungs are essential respiratory organs. Different Obstructive Lung Diseases (OLD) such as bronchitis, asthma, lungs
cancer etc. affects the respiration. Diagnosing OLD in the initial stage is better than diagnosing and curing them later. The delay in diagnosing OLD is due to expensive diagnosing tool and experts requirement. Therefore, a non-invasive diagnosing tool for OLD is required that identifies dysfunctional lungs without the support of expert, complex and expensive diagnosing types of equipment. In this work, we design an Iris based Lungs Prediagnostic System (ILPS). The ILPS takes iris images as input and identifies dysfunctional Lungs based on iridology map and machine learning algorithm .The system works in real-time environment and processes the algorithm on CPU based machine.

Project Objectives

Aim of the project is to avoid conventional metheds of diagnosting such as X-Rays, MRI,CT Scan etc which is harmful for human tissue. Diagnosis of human organs such as lungs  malfunctioning  is also an expensive, complex and time-consuming  process. If not diagnosed well in-time, the outcomes may be fatal. There are multiple factors that affect the early diagnosis that include unavailability of the required technology or approach to such techniques in rural areas and scarcity of relevant experts. A fast, cheap and accurate diagnosis is the need of the hour that can save many precious lives.

Other then above mentioned methods a number of iridology based lungs dysfunctional algorithms
and applications are proposed in the past. These algorithms
are programmed for analysis and do not operate as a standalone system. Designing a real-time stand-alone graphics system that manages an enormous volume of patient images  information and effectively use them to make a decision is a complex and controversial topic. Therefore an
intelligent stand-alone high performance iridology based
disease diagnosis system is required for research and academia to study and analyze different diseases and implement an artificial mechanism to diagnose them.

In this work we propose a non invasive Iris based Lungs Prediagnostic System (ILPS). The proposed system uses artificial intelligent algorithm that can identify the lung malfunction using iridology chart.
 

Project Implementation Method

we propose a non invasive Iris based Lungs Prediagnostic System (ILPS). The proposed system uses artificial
intelligent algorithm that can identify the lung malfunction
using iridology chart. The algorithm takes iris image in realtime and processes algorithm on high performance computing system. The ILPS takes iris images of patients having dysfunctional lungs by using an iridology camera and performs training and testing. During training phase the ILPS takes iris images of healthy and dysfunctional lungs classify subject extract features using gabor filter [11] and label respective features. During the testing phase, the ILPS takes real-time iris image of a person, extracts features using Gabor filter, and by using Support Vector Machine classifier and iridology map, classifies the person as lungs dysfunctional or healthy.
 

Benefits of the Project

• It is non invasive approach
• Real time system
• avoid conventional tools such as X-rays,CT scan etc.
• need no experties to implement.
• Less time consuming and human friendly.

Technical Details of Final Deliverable

Algorithm

The IPLS performs features extraction, features
labeling and classification. Gabor features based blob detector is employed that extracts features based on different color and contrast patterns. Later Support Vector Machine is used that takes the blob features from specific iris segments label the features. Like conventional machine learning algorithm the ILPS algorithm performs training and testing. During trainingthe ILPS algorithm takes trained data-sets of healthy persons and patients and extracts features from each data-set and label them as trained features. During testing mode, the algorithm takes real-time iris image from a subject, extracts features, compares extracted features with the trained features and classifies subject as healthy or lungs patient.

Hardware

The ILPS Hardware Architecture holds multiple processing
nodes. In this project, we practiced three nodes
based high performance computing system. Each node of
the system Architecture utilizes the multi-RISC processor
and GPU-accelerated cores, which comprises General Purpose (Intel/AMD) multi-core processor and Graphical Processing Unit GPU. Each node uses an Intel Xeon X5550 general purpose processor and Nvidia GTX1080 GPU having 2560 Cuda cores. GPUs are used to reduce the cost and power conception and give data level parallelism. The ILPS system architecture uses Linux based operating system and uses open source and easy to program artificial intelligent frameworks.
 

Final Deliverable of the Project

HW/SW integrated system

Type of Industry

Medical , Health

Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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