Detection of Age-Related Macular Degeneration in Fundus Images by an Associative Classifier

Floriano, Andrés García; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar

doi:10.1109/tla.2018.8358676

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Consequently, the technique’s competitive advantage resides in storing information that can be recovered efficiently. New lines of research have emerged with the aim of improving the models [43,44,45,46], which resulted in better performance when recalling previously learned patterns, but at the same time allowed them to associate altered patterns by an additive, subtractive or combined noise that was not presented to memory during their learning. Ritter and Sussaner [47] present the morphological memories which return the qualities of the models of the classic memories, integrating the concepts of dilatation and erosion of mathematical morphology.…”

Section: Methodsmentioning

confidence: 99%

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Márquez‐Olivera

Juárez-Gracia

Hernández-Herrera

et al. 2019

Sensors

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Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

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Section: Methodsmentioning

confidence: 99%

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Márquez‐Olivera

Juárez-Gracia

Hernández-Herrera

et al. 2019

Sensors

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“…For the projects mentioned above, the images were used to test the segmentation methods for conditions related to diseases such as diabetic retinopathy, hypertensive retinopathy, macular degeneration, glaucoma, and retinitis pigmentosa; subsequently, they were used to generate feature vectors from invariant moments [35,36]. Unlike what was performed in the works mentioned earlier, only the images were resized to fit the chosen Deep-Learning model in this research.…”

Section: Datasets For This Workmentioning

confidence: 99%

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

García-Floriano,

Ventura-Molina

2024

Mathematics

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Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through fundus images is age-related macular degeneration, which initially manifests as the appearance of lesions called drusen. Several ways of pre-diagnosing macular degeneration have been proposed, methods based entirely on the segmentation of drusen with prior image processing have been designed and applied, and methods based on image pre-processing and subsequent conversion to feature vectors, or patterns, to be classified by a Machine-Learning model have also been developed. Finally, in recent years, the use of Deep-Learning models, particularly Convolutional Networks, has been proposed and used in classification problems where the data are only images. The latter has allowed the so-called transfer learning, which consists of using the learning achieved in the solution of one problem to solve another. In this paper, we propose the use of transfer learning through the Xception Deep Convolutional Neural Network to detect age-related macular degeneration in fundus images. The performance of the Xception model was compared against six other state-of-the-art models with a dataset created from images available in public and private datasets, which were divided into training/validation and test; with the training/validation set, the training was made using 10-fold cross-validation. The results show that the Xception neural network obtained a validation accuracy that surpasses other models, such as the VGG-16 or VGG-19 networks, and had an accuracy higher than 80% in the test set. We consider that the contributions of this work include the use of a Convolutional Neural Network model for the detection of age-related macular degeneration through the classification of fundus images in those affected by AMD (drusen) and the images of healthy patients. The performance of this model is compared against other methods featured in the state-of-the-art approaches, and the best model is tested on a test set outside the training and validation set.

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“…Computational intelligence algorithms have been widely used for data preprocessing [7][8][9][10][11][12][13][14][15], classification [16][17][18][19][20][21][22][23][24][25][26][27][28], clustering [29][30][31], matching [32][33][34] and prediction [35][36][37], for several disciplines such as education [38][39][40][41][42][43][44][45][46][47][48][49], medicine [30, 50-52], engineering [21,25,34,[53][54][55][56][57], finances [27,58,59] and others [60][61][62][63]…”

Section: Introductionmentioning

confidence: 99%

Electronic Customer Relationship Management in action: an example in business

2020

IJETER

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Nowadays, Electronic Customer Relationship Management (eCRM) constitutes a key aspect for management in the tourism and business industry. The automatic analysis of the available information is a major part of eCRM. In this paper, we address the use of eCRM in the development of tourism and business, and we consider a case of study to determine the usefulness of computational intelligence algorithms. We find that computational intelligence algorithms are very accurate for the determination of Online Shoppers Purchasing Intention, and can be easily applied for marketing purposes.

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Detection of Age-Related Macular Degeneration in Fundus Images by an Associative Classifier

Cited by 4 publications

References 31 publications

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

Electronic Customer Relationship Management in action: an example in business

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