Cervical Cancer Detection Using Single Cell and Multiple Cell Histopathology Images

Arya, Mithlesh; Mittal, Namita; Singh, Girdhari

doi:10.1007/978-981-13-8300-7_17

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“…For the classification, it was used random forest classifier, after comparing the results obtained in other classification techniques. Similar works, [17] and [18] were performed, showing the wide range of projects that seek to apply computational methods to minimize human error in the process of cancer detection.…”

Section: Related Workmentioning

confidence: 82%

Nucleus Detection in Cervical Samples Stained With AgNOR

Gustavo Atkinson Amorim,

Moreno Sanches,

Bottamedi

et al. 2022

Anais Do XIII Computer on the Beach - COTB'22

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Cervical cancer is a public health problem, where the treatment hasa better chance of success if detected early. This paper explores oneway of to analyze argyrophilic nucleolus organizer regions (AgNOR)stained slide using deep learning approaches of object detection fordetecting the different categories of nucleus. Our results show thata balanced dataset between the explored categories was essential,also that a ResNet-50 as backbone of Fast RCNN shows an AP of61.8% and 42.5% to detect nucleus and out of focus nucleus.

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Section: Related Workmentioning

confidence: 82%