Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Features

Jayasingh, Edwin

doi:10.9790/0661-1110107

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…The accuracy of these algorithms relies on multiple parameters and may not be appropriate for all types of images. Even a minor inefficiency in segmentation may mispresent the shape feature prompting less accuracy [ 9 ]. However textural features on the other hand can be obtained precisely.…”

Section: Introductionmentioning

confidence: 99%

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features

Sharma,

Chaudhary,

Patel

et al. 2024

Cureus

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Background Early screening and diagnosis of oral squamous cell carcinoma (OSCC) has always been a major challenge for pathologists. Artificial intelligence (AI)-assisted screening tools can serve as an adjunct for the objective interpretation of Papanicolaou (PAP)-stained oral smears. Aim This study aimed to develop a handy and sensitive computer-assisted AI tool based on color-intensity textural features to be applied to cytologic images for screening and diagnosis of OSCC. Methodology The study included two groups consisting of 80 OSCC subjects and 80 control groups. PAP-stained smears were collected from both groups. The smears were analyzed in Matlab software computed data and color intensity-based textural features such as entropy, contrast, energy, homogeneity, and correlation, were quantitatively extracted. Results In this study, a statistically significant difference was noted for entropy, energy, correlation, contrast, and homogeneity. It was found that entropy and contrast were found to be higher with a decrease in homogeneity, correlation, and energy in OSCC when compared to the control group. Receiver operating characteristic curve analysis was done and accuracy, sensitivity, and specificity were found to be 88%, 91%, and 81%, respectively. Conclusion The gray-level co-occurrence matrix (GLCM) color intensity-based textural features play a significant role in differentiating dysplastic and normal cells in the diagnosis of OSCC. Computer-aided textural analysis has the potential to aid in the early detection of oral cancer, which can lead to improved clinical outcomes.

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Section: Introductionmentioning

confidence: 99%

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features

Sharma,

Chaudhary,

Patel

et al. 2024

Cureus

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A Textural Feature Based Cytological Image Classification Using Artificial Neural Network

Mishro

Agrawal

Sharma

2020

Communications in Computer and Information Science

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Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

Balakrishnan

Subashini

Krishnaveni

2016

Advances in Bioinformatics and Biomedical Engineering

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Automated Segmentation of cell nuclei in Pap smear images plays an important role in the cervical cancer cell analysis systems to make a correct diagnosis decision. The aim of this chapter is to detail about the variety of computational intelligence and image processing approaches developed and used for the nuclei segmentation. In additional, the threshold based segmentation problem is treated as an optimization problem with an objective of preserving both the size and volume of the cell nuclei and also to segment the nuclei region from the original microscopic Pap smear image with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization techniques (ACO). Experimental results are shown, compared in quantitative and qualitative manner as well as the main advantages and limitations of each algorithm are explained.

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Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Features

Cited by 5 publications

References 14 publications

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features

A Textural Feature Based Cytological Image Classification Using Artificial Neural Network

Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images

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