Abnormal condition detection of pancreatic Beta-cells as the cause of Diabetes Mellitus based on iris image

Lesmana, I Putu Dody; Purnama, Iwan; Purnomo, Mauridhi Hery

doi:10.1109/icici-bme.2011.6108614

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…Another major aspect in system performance is that most of the patients do not reveal their true health condition to the researchers that leads to certain kind of bias. Table 3 depicts the comparison of the model proposed in this work with existing iris diagnosis models for detecting broken tissues in the pancreas [1] and for determining pancreas disorder [6]. One can note from Table 3 that an accuracy of 87.5 % has been achieved in the proposed iris diagnosis model with higher sample size.…”

Section: Resultsmentioning

confidence: 84%

“…The pancreas is a fish-shaped, grayish pink gland about 5 in long that stretches across back of the abdomen behind the stomach. Several researchers [1][2][3][4][5][6][7] have utilized iris recognition system along with clinical iridology as an aid to doctors in making their decision. Accuracy of various iridology-based diagnostic models is being illustrated in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Iris recognition system can also be used for clinical applications. A large number of researchers [1][2][3][4][5][6][7] have utilized iris recognition algorithms along with iridology to determine the status of the health of an individual. Iridology, referred to as iris diagnosis [7,8], is an alternate medicine technique through which colors, patterns, and characteristics of iris can be used to gather the information regarding the status of health of an individual.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining diabetes using iris recognition system

Bansal

Agarwal

Sharma

2015

Int J Diabetes Dev Ctries

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Iridology is a science which correlates the apparitions of iris to tissue weaknesses in the body. It merely reveals weaknesses, inflammation, or toxicity in organs or tissues. It also indicates weakness long before the symptoms appear. In this paper, support vector machine-based iris recognition system utilizing iridology has been used to determine diabetes. Features from eye image database of 40 people having healthy eye (normal) and having affected eye (diabetes) have been extracted by 2-D wavelet tree. The overall accuracy is obtained to be 87.50 % which reasonably demonstrates the effectiveness of the system.

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Section: Resultsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining diabetes using iris recognition system

Bansal

Agarwal

Sharma

2015

Int J Diabetes Dev Ctries

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“…The pancreas organ condition can be detected through the iris images of diabetes patients [9]. The beta cells of the pancreas organ using iris diagnosis can measure insulin deficiency [10]. The decision support system is built based on recognizing the sclera from eye images through machine learning algorithms and extracting features from the sclera through color descriptors.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Roy¹,

Mala²

2022

Computer Systems Science and Engineering

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Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean Value, and Mean Standard Deviation are computed after color space conversion from RGB to HSV. Fuzzy k-Nearest Neighbor (F-kNN) is designed for classification. The system proposed is trained and tested using features extracted from jaundiced eye images. The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible, helping clinicians make better decisions for surgical planning.

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“…Studies concerning this matter have been carried out in several methods, such as combining Regression Tree and Random Forest (RF) [4], Fuzzy Hierarchical Model [5], Genetic Programming [6], Support Vector Machines (SVM), Naïve Bayes [7] and artificial neural network [8], [9]. Input data to be in [10], [11], face area [12], [13] and magnetic resonance imaging of the brain [14]. Voice data may also be included as a data input based on several parameters that consist of absolute jitter, shimmer, amplitude perturbation quotient, noise-toharmonic ratio, smoothed amplitude perturbation quotient and relative average perturbation [15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Backpropagation for Early Detection of Diabetes Mellitu

Sofiana¹,

Sutikno²

2018

IJECE

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Diabetes mellitus is one of the urgent health problems in the world. Diabetes is a condition primarily defined by the level of hyperglycemia giving rise to risk of micro vascular damage. Those who suffer from this disease generally do not realize and tend to overlook the early symptoms. Late recognition of these early symptoms may drive the disease to a more concerning level. One solution to solve this problem is to create an application that may perform early detection of diabetes mellitus so that it does not grow larger. In this article, a new method in performing early detection of diabetes mellitus is suggested. This method is backpropagation with three optimization namely early initialization with Nguyen-Widrow algorithm, learning rate adaptive determination, and determination of weight change by applying momentum coefficient. The observation is conducted by collecting 150 data consisting of 79 diabetes mellitus patient and 71 non diabetes mellitus patient data. The result of this study is the suggested algorithm succeeds in detecting diabetes mellitus with accuracy rate of 99.33%. Optimized backpropagation algorithm may allow the training process goes 12.4 times faster than standard backpropagation.

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Abnormal condition detection of pancreatic Beta-cells as the cause of Diabetes Mellitus based on iris image

Cited by 16 publications

References 10 publications

Determining diabetes using iris recognition system

Determining diabetes using iris recognition system

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Optimization of Backpropagation for Early Detection of Diabetes Mellitu

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