The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

Benčo, Miroslav; Kamencay, Patrik; Radilova, Martina; Hudec, Róbert; Šinko, Martin

doi:10.1109/iwssip48289.2020.9145263

Cited by 13 publications

(9 citation statements)

References 24 publications

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“…This formula also provides information about the distance between the samples ( d ). Contrast, correlation, entropy, and homogeneity are some of the most essential features of the general linear correspondence model (GLCM) [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images

Anand

Rane

Bewoor

et al. 2022

Computational Intelligence and Neuroscience

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The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.

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Section: Methodsmentioning

confidence: 99%

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images

Anand

Rane

Bewoor

et al. 2022

Computational Intelligence and Neuroscience

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“…The objective of this experiment is to compare the performance of the proposed method against state-of-the-art deep learning methods. For this purpose, we compared the performance of our method (DMLHP -ESD) against the deep learning systems, i.e., DBN & SAID [42], and CNN [41] on the MIT Vistex image repository, and the results are reported in Table 4. From these results, we can observe that the CNN model [41] achieves the lowest accuracy of 95.28%, whereas the proposed method performs best and obtain the highest accuracy of 98%.…”

Section: F Performance Comparison Against State-of-the-art Deep Learning Methodsmentioning

confidence: 99%

“…For this purpose, we compared the performance of our method (DMLHP -ESD) against the deep learning systems, i.e., DBN & SAID [42], and CNN [41] on the MIT Vistex image repository, and the results are reported in Table 4. From these results, we can observe that the CNN model [41] achieves the lowest accuracy of 95.28%, whereas the proposed method performs best and obtain the highest accuracy of 98%. This comparative analysis illustrates the effectiveness of the proposed method over deep learning models for CBIR.…”

Section: F Performance Comparison Against State-of-the-art Deep Learning Methodsmentioning

confidence: 99%

“…Deep learning-based convolutional neural networks (CNNs) have recently succeeded impressively in computer vision and image processing applications. Different types of deep neural networks have been suggested for image retrieval and classification tasks, such as compact root bilinearconvolutional neural network (CRB-CNN) [36], deep belief network (DBN) [37] method, ensembles CNN [38], Alex Net CNN [39], 1D Grey-Level Co-occurrence Matrix (GLCM)-CNN [41], and Deep belief network (DBN) Stacked autoencoder (SAID).…”

Section: Related Workmentioning

confidence: 99%

“…Miroslav et. al [41] presented 1D GLCM with 25 layers of CNN. In [42], the image retrieval problem was examined using two deep learning approaches: DBN SAID.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Khan

Javed²,

Mahmood

et al. 2021

IEEE Access

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Social media platforms such as Twitter, Facebook, and Flicker, and the evolution of digital image capturing devices have resulted in the generation of a massive number of images. Thus, we experienced exponential growth in digital image repositories in the last decade. Content-based image retrieval (CBIR) has been extensively employed to reduce the dependency on textual annotations for image searching. Effective feature descriptor is mandatory to retrieve the most relevant images from the repository. Additionally, CBIR methods often experience the semantic gap problem, which must be addressed. In this paper, we propose a novel texture feature descriptor, Directional Magnitude Local Hexadecimal Patterns (DMLHP), based on texture orientation and magnitude to retrieve the most relevant images. The objective of the proposed feature descriptor is to examine the relationship of neighboring pixels with their adjacent neighbors based on texture orientation and magnitude. Our DMLHP texture descriptor is capable of capturing the texture and semantic information of the images effectively with the same visual content. Furthermore, the proposed method employs a learning-based approach to lessen the semantic gap problem and to improve the understanding of the contents of query images to retrieve the relevant images. The presented descriptor provides remarkable results by achieving the average retrieval precision (ARP) of 66%, 92%, 83%, average retrieval recall (ARR) of 66%, 92%, 83%, average retrieval specificity (ARS) of 99%, 99%, 76%, and average retrieval accuracy (ARA) of 98%, 99%, 85% on the AT&T, MIT Vistex, and Brodatz Texture image repositories, respectively. Our experiments reveal that the proposed DMLHP descriptor achieves far better performance, i.e., 95% on AT&T, 92% on BT, and 99% on MIT Vistex, when used with a learning-based approach over a non-learning-based approach (similarity measure). Experimental results show that the proposed texture descriptor outperforms state-of-the-art descriptors such as LNIP, LTriDP, LNDP, LDGP, LEPSEG, and CSLBP for CBIR.INDEX TERMS Content-based image retrieval, Orientation-based pattern, Magnitude-based pattern, Similarity-based approach, Learning-based approach.

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The Effect of Different Feature Selection Methods for Classification of Melanoma

Maiti¹,

Chatterjee

2021

Recent Trends in Signal and Image Processing

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The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

Cited by 13 publications

References 24 publications

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

The Effect of Different Feature Selection Methods for Classification of Melanoma

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