Digital Repository

Diagnosing Cervical Cancer with Deep Learning

Show simple item record

dc.contributor.advisor GOEL, PRANAY en_US
dc.contributor.author BHARTIYA, SNEHAL en_US
dc.date.accessioned 2020-06-18T10:42:43Z
dc.date.available 2020-06-18T10:42:43Z
dc.date.issued 2020-04 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4772
dc.description.abstract Cervical cancer ranks as the fourth most prevalent cancer worldwide, affecting mostly the developing countries. However, early diagnosis can help facilitate the clinical management of the patient. The problem lies in the sparse presence of qualified and professional health cytotechnicians as compared to the number of people that need to be diagnosed. Computer-Aided Diagnostic system can be of a lot of help in making the diagnosis more accurate, reliable, faster and cheaper. Most of the existing algorithms require precise image segmentation to distinguish the cell. The traditional machine learning diagnostic system work similarly to the cytopathologists who rely on handcrafted morphological features such as nucleus area, nucleus-cytoplasm perimeter ratio, etc to determine the malignancy in a cell. However, our study uses Convolutional Neural Networks(CNN) which could potentially allow us to eliminate the computationally expensive tasks of segmentation and feature selection. Our results evidenced best accuracy scores of 96.25% for binary classification and 66.87% for seven class classification, which are comparable to the results achieved with established Machine Learning techniques. This study addresses the different aspects of training Deep networks on a publicly available cervical cancer database by Herlev Hospital. We also did a comparative investigation to establish the most suitable working hyperparameters, optimizers and classifiers for the dataset. en_US
dc.language.iso en en_US
dc.subject Deep Learning en_US
dc.subject Biology en_US
dc.subject CNN en_US
dc.subject Cervical Cancer en_US
dc.subject 2020 en_US
dc.title Diagnosing Cervical Cancer with Deep Learning en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Biology en_US
dc.contributor.registration 20151023 en_US


Files in this item

This item appears in the following Collection(s)

  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

Show simple item record

Search Repository


Advanced Search

Browse

My Account