Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

Nawandhar, Archana A.; Kumar, Navin; Veena, R.; Yamujala, Lakshmi

doi:10.1016/j.bspc.2019.101671

Cited by 20 publications

(10 citation statements)

References 28 publications

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“…Transform-based approach mainly used watershed segmentation [18,23,30,84,86,103]. Cluster-based segmentation includes k-means clustering, fuzzy c-means clustering, and DBSCAN [14,31,34,67,82]. The literature also includes single-pass voting [10], region-based segmentation [71,72], graph-based segmentation [70], deep network segmentation models [27,37], and connected component analysis [56].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…In earlier days, only cells and nuclei shape features were used for classification [22]. However, statistical features, Haralick texture features, fractal dimension, and color features were used in later days [14,19,25]. A few researchers used small dataset and achieved good results [25,32,35,40,56,57].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…H&N SCC develops in mucosal linings of upper aerodigestive tract, tongue, oral cavity, pharynx, larynx, trachea, nasal cavity, and paranasal sinuses [4]. Oral SCC (OSCC) was observed in 95% cases of oral cancer and is the most common among H&N region [14]. The higher incidence of oral cancer is due to the late diagnosis of potential precancerous lesions.…”

Section: Head and Neck Sccmentioning

confidence: 99%

“…Almangush et al [24] discussed different WHO grading and staging systems, and highlighted the importance of AI to improve staging, grading, and treatment planning of OSCC. Rahman et al [25] and Nawandhar et al [14] proposed a ML-based classifier to classify normal cells and oral SCC. Further, they graded OSCC into three grades based on texture features, gradient features, geometrical features, and Tamura features.…”

Section: Oral Cancer Grade Classificationmentioning

confidence: 99%

“…Further, they graded OSCC into three grades based on texture features, gradient features, geometrical features, and Tamura features. In [14], Nawandhar et al classified 676 OSCC microscopic images using maximum voting method and obtained 95.56% accuracy. A total of 305 features were extracted after applying contrast limited adaptive histogram equalization (CLAHE) technique.…”

Section: Oral Cancer Grade Classificationmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists ′ evaluations.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Head and Neck Sccmentioning

confidence: 99%

Section: Oral Cancer Grade Classificationmentioning

confidence: 99%

Section: Oral Cancer Grade Classificationmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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Image retrieval‐based parenchymal analysis for breast cancer risk assessment

et al. 2021

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This research on breast cancer risk assessment aims to develop models that predict the likelihood of breast cancer. In recent years, the computerized analysis of visual texture patterns in mammograms,namely parenchymal analysis, has shown great potential for risk assessment. However, the visual complexity and heterogeneity of visual patterns limit the performance of parenchymal analysis in large populations. In this work, we propose a method to create individualized risk assessment models based on the radiological visual appearance (radiomic phenotypes) of the mammograms. Methods: We developed a content-based image retrieval system to stratify mammographic analysis according to the similarities of their radiomic phenotypes. We collected 1144 mammograms from 286 women following a casecontrol study design. We compared the classical parenchymal analysis with the proposed approach using the area under the ROC curve (AUC) with 95% confidence intervals (CI). Statistical significance was assessed using DeLong's test (p <0.05). Results: At a patient level,AUC values of 0.504 (95% CI:0.398-0.611) with classical parenchymal analysis increased to 0.813 (95% CI: 0.734-0.892) when the radiomic phenotypes are incorporated with the proposed method. In risk estimation from individual, standard mammographic views, the highest performance was obtained with the mediolateral oblique view of the right breast (RMLO), with an AUC value of 0.727 (95% CI: 0.634-0.820). Differences in performance among views were statistically significant (p < 0.05) Conclusions: These results indicate that the utilization of radiomic phenotypes increases the performance of computerized risk assessment based on parenchymal analysis of mammographic images. Significance: The creation of individualized risk assessment models may be leveraged to target personalized screening and prevention recommendations according to the person's risk.

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Single-stage object detector with attention mechanism for squamous cell carcinoma feature detection using histopathological images

Prabhu,

Prasad,

et al. 2023

Multimed Tools Appl

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Squamous cell carcinoma is the most common type of cancer that occurs in squamous cells of epithelial tissue. Histopathological evaluation of tissue samples is the gold standard approach used for carcinoma diagnosis. SCC detection based on various histopathological features often employs traditional machine learning approaches or pixel-based deep CNN models. This study aims to detect keratin pearl, the most prominent SCC feature, by implementing RetinaNet one-stage object detector. Further, we enhance the model performance by incorporating an attention module. The proposed method is more efficient in detection of small keratin pearls. This is the first work detecting keratin pearl resorting to the object detection technique to the extent of our knowledge. We conducted a comprehensive assessment of the model both quantitatively and qualitatively. The experimental results demonstrate that the proposed approach enhanced the mAP by about 4% compared to default RetinaNet model.

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Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

Cited by 20 publications

References 28 publications

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Image retrieval‐based parenchymal analysis for breast cancer risk assessment

Single-stage object detector with attention mechanism for squamous cell carcinoma feature detection using histopathological images

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