Deep Learning Approach for Classifying Papanicolau Cervical Smears

Martínez-Más, José; Bueno-Crespo, Andrés; Imbernón, Baldomero; Cecilia, José M.; Martínez‐España, Raquel; Remezal-Solano, Manuel; Sánchez-Espinosa, Alberto; Ortiz-Reina, Sebastián; Martínez-Cendán, Juan-Pedro

doi:10.1145/3229710.3229732

Cited by 3 publications

(1 citation statement)

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“…By eliminating interfering factors, the application of such a model is narrowed in clinical practice. In order to overcome this problem, Martinez-Mas et al proposed Cell Merger Approach combined with convolutional neural network model and showed that by creating a dataset with overlapped and folded cells and applying deep model for classification, accuracy of 88.8%, sensitivity of 0.92 and specificity of 0.83 can be achieved [21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm for Cervical Cancer Detection Based on Images of Optomagnetic Spectra

Jeftić

Hut

Stanković

et al. 2022

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In order to further investigate performance of Optomagnetic Imaging Spectroscopy in cervical cancer detection, deep learning algorithm has been used for classification of optomagnetic spectra of the samples. Optomagnetic spectra reflect cell properties and based on those properties it is possible to differentiate normal cells from cells showing different levels of dysplasia and cancer cells. In one of the previous research, Optomagnetic imaging spectroscopy has demonstrated high percentages of accuracy, sensitivity and specificity in cervical cancer detection, particularly in the case of binary classification. Somewhat lower accuracy percentages were obtained in the case of four class classification. Compared to the results obtained by conventional machine learning classification algorithms, proposed deep learning algorithm achieves similar accuracy results (80%), greater sensitivity (83.3%), and comparable specificity percentages (78%).

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Section: Introductionmentioning

confidence: 99%