Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

Christobel, J. Shiny; Vimala, D.; Athanesious, J. Joshan; Singh, S. Christopher Ezhil; Sivaraj, M.

doi:10.1155/2022/4802872

Cited by 5 publications

(4 citation statements)

References 21 publications

(17 reference statements)

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“…Among the algorithms that work in this way are SVM, KNN, Naive Bayes, etc. Works such as these have been published by several authors and have appeared in [ 54 , 55 ]. Use of CNN networks: This approach eliminates the need to extract feature vectors, since CNN networks can extract such features through feature maps with their convolutional layers.…”

Section: Related Workmentioning

confidence: 99%

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Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Bragança,

Torres,

Macedo

et al. 2024

Diagnostics

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The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Among the algorithms that work in this way are SVM, KNN, Naive Bayes, etc. Works such as these have been published by several authors and have appeared in [ 54 , 55 ].…”

Section: Related Workmentioning

confidence: 99%

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Bragança,

Torres,

Macedo

et al. 2024

Diagnostics

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show abstract

“…Dentre os algoritmos que trabalham desta maneira, cita-se a SVM, KNN, Naive Bayes, etc. Trabalhos como estes foram publicados por diversos autores como também em [47,48].…”

Section: Reconhecimento De Padrões Glaucomatososunclassified

Inteligência artificial e diagnóstico do glaucoma

Bragança,

Torres,

Soares

2023

BASR

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O progresso dos algoritmos de inteligência artificial (IA) no processamento de imagens digitais e nos estudos de diagnóstico automático da doença ocular glaucoma vem crescendo e apresentando avanços essenciais para garantir um melhor atendimento clínico a população. Diante do contexto, este artigo descreve os principais tipos de glaucoma existentes e as formas tradicionais de diagnóstico. Apresenta também a epidemiologia mundial da doença e como os estudos com algoritmos de IA vêm sendo investigados como uma possível ferramenta para auxiliar no diagnóstico precoce desta patologia por meio de triagens populacionais. Portanto, a seção de trabalhos relacionados apresenta os principais estudos e metodologias utilizadas na classificação automática do glaucoma a partir de imagens digitais de fundo de olho e algoritmos de IA, bem como as principais bases de dados contendo imagens rotuladas para o glaucoma e disponíveis publicamente para treinamento dos algoritmos de aprendizado de máquinas.

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“…However, the explosive growth of the distribution of real physical visual data presents significant characteristics of high complexity, dynamism and data privacy. Therefore, there are highly demanding for higher physical requirements for existing artificial intelligence models, algorithms and system frameworks [6].…”

Section: Introductionmentioning

confidence: 99%

Research on image features extraction based on machine learning algorithms

Chang

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.

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Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

Cited by 5 publications

References 21 publications

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Inteligência artificial e diagnóstico do glaucoma

Research on image features extraction based on machine learning algorithms

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