Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Huang, Ching-Lien; Lian, Meng Jia; Wu, Yi; Chen, Wei Ming; Chiu, Wen Tai

doi:10.3390/diagnostics10060389

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…The obtained results showed that it is possible to predict the presence of early-stage breast cancer with high spatial resolution using SWIR and the phantom model in which the optical parameters are implemented in the breast structure [ 3 ]. Huang and colleagues proposed a promising method based on the gray level co-occurrence matrix (GLCM) image processing model to achieve a rapid technique with a more reliable diagnostic performance for various types of chemoresistance for the cisplatin of human ovarian adenocarcinoma cells by feature extraction of GLCM [ 4 ].…”

mentioning

confidence: 99%

Optical Diagnostics in Human Diseases

Dunaev

2021

Diagnostics

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mentioning

confidence: 99%

Optical Diagnostics in Human Diseases

Dunaev

2021

Diagnostics

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“…The values are evaluated on repeated execution of the proposed model with a varied training level. The performance of the proposed model is compared against a Heuristic Approach for Real-Time Image Segmentation (HARIS) [ 25 ], a Fine-Tuned Neural Networks (FTNN) approach [ 77 ], a Convolutional Neural Network (CNN) [ 32 ], the VGG-19 model [ 78 ], and MobileNet models [ 72 , 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…One strategy of texture attribute extraction is the Grey-Level Co-occurrence Matrix (GLCM) [ 72 ] approach with the localized intensity coefficient’s recurring sequence. GLCM gives the spatial distribution structure of the color and intensity of the pixel, which is determined by the distribution of intensity levels within the window.…”

Section: Methodsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

488

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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“…Huang et al . (52) also used the GLCM approach to identify textural features on ovarian adenocarcinoma cells indicative of chemoresistance. Specifically, the GLCM was used to calculate contrast, energy, entropy, and homogeneity, variables that collectively can reveal the disordered surface morphology characteristic of cancer cells.…”

Section: Discussionmentioning

confidence: 99%

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Chéles

Ferreira

Jesus

et al. 2022

Preprint

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OBJECTIVE: To study the application of image processing for segmentation of blastocysts images and extraction of potential variables for prediction of embryo fitness. DESIGN: Retrospective study. SETTING: Single reproductive medical center. IVI-RMA (Valencia, Spain) between 2017 and 2019. PATIENTS: An initial dataset including 353 images from EmbryoScope and 474 images from Geri incubators was acquired, of which 320 images from EmbryoScope and 309 images from Geri incubators were used in this study. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful segmentation of images into trophectoderm (TE), blastocoel, and inner cell mass (ICM) using the proposed processing steps. RESULTS: A total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the expanding blastocyst (EX), ICM, or TE. These variables can be categorized into texture, gray level average, gray level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. CONCLUSIONS: The proposed image processing protocol that can successfully segment human blastocyst images from two distinct sources and extract 33 variables with potential utility in embryo selection for ART.

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Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Cited by 13 publications

References 26 publications

Optical Diagnostics in Human Diseases

Optical Diagnostics in Human Diseases

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

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