dc.contributor.advisor | Alam, Md. Golam Rabiul | |
dc.contributor.author | Bhuiyan, Mazedul Haque | |
dc.contributor.author | Tabassum, Fariba | |
dc.contributor.author | Bushra, Umme | |
dc.contributor.author | Shwon, Md.Mahbub Rahman | |
dc.date.accessioned | 2021-06-02T04:28:19Z | |
dc.date.available | 2021-06-02T04:28:19Z | |
dc.date.copyright | 2020 | |
dc.date.issued | 2020-04 | |
dc.identifier.other | ID: 15201035 | |
dc.identifier.other | ID: 15201038 | |
dc.identifier.other | ID: 15201034 | |
dc.identifier.other | ID: 15201044 | |
dc.identifier.uri | http://hdl.handle.net/10361/14464 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 37-39). | |
dc.description.abstract | Uterine cervical cancer is the second most regular gynecological harm around the
world. The appraisal of the degree of sickness is fundamental for arranging ideal
treatment. Imaging procedures are progressively utilized in the pre-treatment workup of cervical malignancy[22]. Presently, MRI for the neighborhood degree of sickness assessment and PET-check for removed ailment appraisal is considered as firstline procedures. In any case, over the most recent couple of years, ultrasound has
picked up consideration as an imaging system for assessing ladies with cervical cancer.In this paper, we will take a shot at the advancement of a profound conviction
system to order ultrasound pictures of the cervical cells to recognize cervical malignant growth.
This postulation talks about the depiction of examples of single pap-smear cells from
a current database set up at Herlev University Hospital. Wellbeing, the apparatus
ought to be utilized before disease improvement to distinguish pre-dangerous cells
in the uterine cervix[16]. For the separation among ordinary and unusual cells, open
cell qualities, for example, area, position, and splendor of the core and cytoplasm are
utilized. The exhibition of the classifier is determined on the rate total blunder but
on the other hand is tried on the recurrence of bogus negative and bogus positive
mistakes. The point is to support the all out mistake instead of the outcomes
acquired already.
A detailed overview of Herlev University Hospital’s latest pap- data prepares a new
comparison platform Papsmear for publishing on Twitter. The papsmear collection
consists of 917 experiments on 7 separate types of normal and irregular cells spread
unequally. Every sample is represented by 20 characteristics. The average performance of the tested classifiers indicates no substantial change in earlier tests, but
similar results are obtained using very basic methods. | en_US |
dc.description.statementofresponsibility | Mazedul Haque Bhuiyan | |
dc.description.statementofresponsibility | FaribaTabassum | |
dc.description.statementofresponsibility | Umme Bushra | |
dc.description.statementofresponsibility | Md.Mahbub Rahman Shwon | |
dc.format.extent | 39 Pages | |
dc.language.iso | en_US | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Cervical Cancer | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Cervix | en_US |
dc.subject | Prediction | en_US |
dc.subject | PCA | en_US |
dc.subject | t-SNE | en_US |
dc.subject | AUC-ROC Curve | en_US |
dc.subject | XGBooster | en_US |
dc.subject | SVM | en_US |
dc.subject | CNN | en_US |
dc.title | Cervical Cancer Detection from Cervix Image Using Pap smear Imaging through CNN | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |