Automated classification of Pap smear images to detect cervical dysplasia

Bora, Kangkana; Chowdhury, Manish; Mahanta, Lipi B.; Kundu, Malay K.; Das, Anup Kumar

doi:10.1016/j.cmpb.2016.10.001

Cited by 157 publications

(104 citation statements)

References 24 publications

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“…Unfortunately, though, little practical progress has been made toward a truly effective screening system that might be used in parts of the world where cytotechnologists are scarce. Although many publications have compared different forms of image analysis using this sets of pre‐selected images, so far only limited steps have been taken toward trying to analyze entire slides . Until the algorithms developed during the course of this research achieve demonstrably high levels of sensitivity and specificity when applied to entire samples from patients, the exercise will remain largely academic.…”

Section: Papnetmentioning

confidence: 99%

Automated screening of Papanicolaou tests: A review of the literature

Thrall

2018

Diagnostic Cytopathology

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Automated Papanicolaou test screening systems have now been available for over 25 years. Currently two automated screening systems are in widespread clinical use. These are the ThinPrep Imaging System and the FocalPoint GS Imaging System. In their current configurations, both facilitate faster screening by showing a limited number of fields of view (FOV) to cytotechnologists. The FOV are based on the use of proprietary algorithms applied to computerized images of the slide that determine the cells and cell groups with the highest likelihood of abnormality. If all of the FOV are deemed to be negative, the case can be signed out with no additional review; if one or more fields appear possibly abnormal, the entire slide must be manually screened. The United States Food and Drug Administration has ruled that for workload calculation purposes, looking at only the FOV review counts as one-half slide, potentially greatly increasing the number of slides that can be screened. However, follow-up studies of this technology have shown that screening accuracy declines when very large numbers of cases are reviewed per day. Recommendations designed to limit screening volumes to levels that do not jeopardize patient care have therefore been created. The development of fully automated screening that does not rely on human judgment remains an unrealized aspiration. This review covers the history of the development and clinical implementation of automated screening technology with descriptions of the various automated screening systems and their performance as reported in published literature.

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Section: Papnetmentioning

confidence: 99%

Automated screening of Papanicolaou tests: A review of the literature

Thrall

2018

Diagnostic Cytopathology

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“…Machine learning models for health are often based on convolutional neural networks (CNNs); the type of deep learning algorithms commonly applied to image classification and segmentation . Applications directly relevant to women's reproductive health include: reproductive medicine; obstetric imaging; breast imaging; and cervical cancer screening …”

Section: Machine Learning In Women's Healthmentioning

confidence: 99%

“…[4][5][6] Applications directly relevant to women's reproductive health include: reproductive medicine 7 ; obstetric imaging 8,9 ; breast imaging 4,10 ; and cervical cancer screening. [11][12][13] In their recent study published in the Journal of the National Cancer Institute, Hu and colleagues assessed the performance of a machine learning model to evaluate cervical images for cancer screening. 14…”

Section: Machine Learning In Women's Healthmentioning

confidence: 99%

Contemporary Issues in Women's Health

Geary

Chibwesha

Stringer

2019

Intl J Gynecology & Obste

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“…Pada citra biner, area didefinisikan sebagai jumlah piksel yang berada di dalam region [2][9] [22]. Area juga didefinisikan sebagai zero-order moment dari objek biner [14].…”

Section: B Areaunclassified

“…Perimeter juga didefinisikan sebagai banyaknya titik pembatas pada suatu objek [25]. Perimeter merepresentasikan banyaknya piksel yang mewakili batas objek [22].…”

Section: Compactnessunclassified

Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Sholik

Fatichah

2017

JUTI

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ABSTRAK Perubahan orientasi objek pada saat akuisisi memerlukan metode ekstraksi fitur yang invariant terhadap rotasi. Ekstraksi fitur tekstur yang telah digunakan dalam kombinasi fitur sebelumnya untuk klasifikasi sel serviks pada dataset Herlev antara lain homogenitas GLCM dan Local Binary Pattern Histogram Fourier (LBP-HF I. PENDAHULUANANKER serviks merupakan penyebab utama keempat kematian kanker pada wanita di dunia [1]. Hal ini dapat dicegah jika diperiksa pada tahap prakanker [2]. Kanker serviks terjadi karena adanya perkembangan sel-sel abnormal secara cepat dan tidak terkendali pada leher rahim [3]. Papanicolaou test adalah langkah pertama dalam pemeriksaan kanker serviks [3]. Hasil pemeriksaan akan menunjukkan kondisi sel serviks normal atau abnormal. Pemeriksaan terdiri dari pengumpulan sampel sel dari daerah tertentu pada serviks, kemudian sampel sel diamati melalui mikroskop dan didiagnosis oleh ahli sel secara manual [4]. Analisis hasil pemeriksaan secara manual memerlukan waktu yang lama dan rawan terhadap kesalahan sehingga dibutuhkan proses klasifikasi sel serviks pada citra pap smear berbasis komputer.Adanya perubahan orientasi sel pada saat akuisisi akan mempengaruhi nilai fitur yang dihasilkan [5] sehingga menjadi dasar pada penelitian ini. Tidak semua fitur dapat menghasilkan fitur yang sama jika citra sel yang sama mengalami perubahan orientasi sehingga dibutuhkan metode ekstraksi fitur yang invariant terhadap rotasi.

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Automated classification of Pap smear images to detect cervical dysplasia

Cited by 157 publications

References 24 publications

Automated screening of Papanicolaou tests: A review of the literature

Automated screening of Papanicolaou tests: A review of the literature

Contemporary Issues in Women's Health

Klasifikasi Sel Serviks Pada Citra Pap Smear berdasarkan Fitur Bentuk Deskriptor Regional dan Fitur Tekstur Uniform Rotated Local Binary Pattern

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