Content-based and Knowledge-enriched Representations for Classification Across Modalities: A Survey

Pittaras, Nikiforos; Giannakopoulos, George; Stamatopoulos, Panagiotis; Karkaletsis, Vangelis

doi:10.1145/3583682

Cited by 2 publications

(2 citation statements)

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“…After image pre-processing feature extraction achieved using GLCM global descriptor, GLCM retrieved additional information from its layers to find the precise photos from the repository, their characteristics in terms of parameters such as distance, angle, resolution, level, and symmetric values. These values are retrieved from the pictures using the GLCM technique by image attributes such as Dissimilarity, Correlation, Homogeneity, Contrast, ASM, and Energy (1)(2)(3) . The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers.…”

Section: Additional Feature Extractionmentioning

confidence: 99%

“…First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the dependability of deep CBIR systems (3,4) . The quality of picture searches by using a strategy that uses multiple deep neural networks and K-Nearest Neighbor algorithms in content-based image retrieval (5) .…”

Section: Introductionmentioning

confidence: 99%

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An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

Vasudeva,

Chandrashekara

2023

IJST

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Objectives:The objective of this work is to obtain an efficient medical image retrieval and classification from a larger healthcare datasets using Novel approach. Methods: In this study five different classes of Medical images are taken for input, features are extracted using GLCM (Grey Level Co-occurrence Matrix) by image attributes such as dissimilarity, correlation, homogeneity, contrast, ASM, and energy. The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers. The received feature vectors are input into the most often used deep learning models Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) for image classification. Then CNN model is integrated with a deep learning model based on Long-Short Term Memory (LSTM), which incorporates additional layers into its structure and works on large datasets. Further the retrieval performance is improved by Euclidean Distance Technique. Findings: Performance evaluation is performed by comparing and analyzing the experimental findings of proposed methods, ANN, CNN and CNN-LSTM yields the retrieval accuracy of 97.79%, 98.78% and 99.4%. The Precision, Recall and F1-Score are also compared, and they are more accurate when picture classification is performed on larger healthcare datasets. Novelty: The additional feature extraction using GLCM and the proposed hybrid model can extract better medical image features, and achieve higher classification accuracy compared with earlier image classification models.

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Section: Additional Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%