Pre-Diagnostic Tool to Predict Obstructive Lung Diseases Using Iris Recognition System

Bansal, Atul; Agarwal, Ravinder; Sharma, Rohini

doi:10.1007/978-981-10-8968-8_7

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…One of the most outstanding application areas for the SVMs is biometrics [13], [14], [24]- [28]. For example, the SVM-based model has been widely used for face verification and recognition by using Local Binary Patterns (LBP) as feature extractor [26].…”

Section: Black-box Approachmentioning

confidence: 99%

“…For example, the SVM-based model has been widely used for face verification and recognition by using Local Binary Patterns (LBP) as feature extractor [26]. In the same vein, SVM has widely been used as classifier for iris and fingerprint authentication/verification and identification/recognition [13], [14], [27], [28].…”

Section: Black-box Approachmentioning

confidence: 99%

“…The idea is to use a database d 1 for training the model and use this database d 1 as internal validation, and after that, the second database d 2 is used as external validation. This type of validation is widely used in biometrics problems such as iris and fingerprint identification [13], [14], [24]- [28], [164].…”

Section: Measuring the Effectiveness Of The Applied Modelmentioning

confidence: 99%

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

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Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a stateof-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and blackbox, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications. INDEX TERMS Black-box, white-box, explainable artificial intelligence, deep learning.

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Section: Black-box Approachmentioning

confidence: 99%

Section: Black-box Approachmentioning

confidence: 99%

Section: Measuring the Effectiveness Of The Applied Modelmentioning

confidence: 99%

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

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Retracted: Computational Measuring Approach for the Identification of Probable Intestinal System Pathologies through the Human Iris Parameters

Miranda

Salinas

2019

2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA)

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Screening and validation of the iris manifestation among patients with hemiplegia – an observational study

Mohamed

Vijayakumar²,

Manavalan

et al. 2023

Journal of Complementary and Integrative Medicine

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Objectives Understanding and validating the science behind traditional diagnostic methods is a niche area to be explored. Iris diagnosis is one such valuable diagnostic tool used in Naturopathy. In the current study, we have assessed and documented the iris changes observed among patients with hemiplegia with respect to the iridology chart. Methods We recruited 35 patients with hemiplegia which includes both genders. Iris image was captured by Angel Kiss New 5.0MP Iridology Camera with Pro Iris Analysis Software. Lesion characters, such as open lesion, closed lesion, spot, furrow, radii solaris, intestinal crypts etc., in the iris were noted along with its various characteristics in an excel sheet in numerical order for analysis. Results Majority of the included patients were male (n=30) and the mean age of the patients was 46 years. The most common iris lesions noted were radii solaris and intestinal crypt in the cerebrum and cerebellum regions. Other notable lesions include open lesion, closed lesion, brown spot, hole and nerve ring. ROC analysis for selected iris lesions showed that intestinal crypt (AUC=0.82, p=0.01) and radii solaris (AUC=0.62, p=0.04) to be potential markers for hemiplegia. Conclusions Two common lesions observed in patients with hemiplegia are radii solaris and intestinal crypt, corresponding to the cerebrum and cerebellum regions of the iris. Future studies with larger sample size would help warrant our current findings, and also the possible application of iris diagnosis as an early diagnostic marker for hemiplegia.

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Pre-Diagnostic Tool to Predict Obstructive Lung Diseases Using Iris Recognition System

Cited by 4 publications

References 14 publications

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Retracted: Computational Measuring Approach for the Identification of Probable Intestinal System Pathologies through the Human Iris Parameters

Screening and validation of the iris manifestation among patients with hemiplegia – an observational study

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