Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

Presenting an intelligent, machine learning-based method with commercialization capabilities for identifying diseases from human eye iris images

Document Type : The scientific research paper

Authors
Avaz Naghipour
Ramin Shahi
Department of Computer Engineering, University College of Nabi Akram,Tabriz, Iran.
JABM.3.2.15564.351256.326968
Abstract
From the past until today, accurate and early diagnosis of disease symptoms has been of interest to everyone. In this regard, the iridology has attracted the attention of doctors and experts due to its high speed in the early diagnosis of possible diseases. In iridology, the iris of the eye is divided into different parts and each part is assigned to a specific organ. The presence of some characteristics in each of these parts indicates the presence of a possible disease in the relevant organ. On the other hand, smart and modern medicine is expanding more and more due to its ease, speed and accuracy compared to traditional medicine. Nowadays, with the increasing progress of artificial intelligence algorithms and machine learning and the increase of its general applications, the use of artificial intelligence in medical fields has become the top of researchers' attention. In line with the automatic diagnosis of diseases and smart medicine, in this work, a new method using deep neural networks and image processing methods is presented to diseases detection from images based on iridology science. In the proposed method, human eye images are fed into a convolutional neural network. This network, which is designed with the structure of encoder/decoder networks, classifies the pixels of the input image that represent the iris from non-iris pixels. Then, with the mask of iris pixels with the input image, the area related to the iris is extracted from the eye image. After extracting the iris, the characteristics of the disease are detected by applying an adaptive thresholding method and then cropping the image according to the iridology chart. Finally, according to the characteristics of the disease in the part related to which organ, a decision is made about the type of possible disease. The proposed method has been evaluated by several widely used datasets in the field of iris image processing. The datasets include pictures taken with special cameras and also pictures taken with cellphones. The accuracy of the proposed method in locating the iris is almost 99%.
Keywords
Iridology
Deep Neural Networks
Image processing
Machine Learning
Disease Detection

Subjects

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Journal of Intelligent Marketing Management
Volume 6, Issue 3 - Serial Number 29
Summer 2025
Pages 188-205

  • Receive Date 26 May 2025
  • Revise Date 09 July 2025
  • Accept Date 11 July 2025