Early detection of cervical cancer using deep neural networks
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2023
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| Accès en ligne: | http://hdl.handle.net/10361/18093 |
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10361-180932023-04-06T21:01:45Z Early detection of cervical cancer using deep neural networks Akhund, Atoshi Ahmad, Saad Taki, Sarwar Siddiqui Alam, Md. Ashraful Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Cervical cancer HPV Precancerous lesions Cervix image Deep neural network VGG ResNet Inception Neural networks (Computer science) Cervix uteri--Cancer--Diagnosis This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-33). Cervical cancer is a disease that is mostly preventable, but it is one of the major causes of cancer fatality in women worldwide. Several studies say that annually 2,60,000 women die because of cervical cancer. Chronic infections with ”high-risk (HR)” human papillomavirus are the leading cause of cervical cancer (HPV). Regular cervical cancer screening, on the other hand, can help to prevent this dangerous disease. Cervical cancer screening is a procedure for detecting precancerous and cancer in women who are at risk, and it is recommended for all women aged 30 to 49. Cervical cancer can be avoided if precancerous lesions are detected and treated early. Nowadays, several tests are performed to detect cervical cancer, most of whom are time consuming and expensive. In this paper, we are approaching the development of a fast and effective system to detect cervical cancer from the cervix image in a minimum time with better accuracy using deep neural networks. First, we collected image data and classified them using VGG16, VGG19, InceptionV3, ResNet50 and ResNet101. From our result we got an accuracy rate of 88.48% from VGG16, 88.97% from VGG19, 88.09% from InceptionV3, 88.67% from ResNet50 and 89.06% from ResNet101. Then, using a mixture of classifiers with the greatest accuracy, we created ensemble models with the best overall accuracy rate of 94.20 percent for CERVIXEN V1, 95.01 percent for CERVIXEN V2, and 94.69 percent for CERVIXEN V3. Atoshi Akhund Saad Ahmad Sarwar Siddiqui Taki B. Computer Science 2023-04-06T05:26:27Z 2023-04-06T05:26:27Z 2022 2022-05 Thesis ID 18201199 ID 18101226 ID 18101193 http://hdl.handle.net/10361/18093 en 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. 33 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Cervical cancer HPV Precancerous lesions Cervix image Deep neural network VGG ResNet Inception Neural networks (Computer science) Cervix uteri--Cancer--Diagnosis |
| spellingShingle |
Cervical cancer HPV Precancerous lesions Cervix image Deep neural network VGG ResNet Inception Neural networks (Computer science) Cervix uteri--Cancer--Diagnosis Akhund, Atoshi Ahmad, Saad Taki, Sarwar Siddiqui Early detection of cervical cancer using deep neural networks |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Akhund, Atoshi Ahmad, Saad Taki, Sarwar Siddiqui |
| format |
Thesis |
| author |
Akhund, Atoshi Ahmad, Saad Taki, Sarwar Siddiqui |
| author_sort |
Akhund, Atoshi |
| title |
Early detection of cervical cancer using deep neural networks |
| title_short |
Early detection of cervical cancer using deep neural networks |
| title_full |
Early detection of cervical cancer using deep neural networks |
| title_fullStr |
Early detection of cervical cancer using deep neural networks |
| title_full_unstemmed |
Early detection of cervical cancer using deep neural networks |
| title_sort |
early detection of cervical cancer using deep neural networks |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18093 |
| work_keys_str_mv |
AT akhundatoshi earlydetectionofcervicalcancerusingdeepneuralnetworks AT ahmadsaad earlydetectionofcervicalcancerusingdeepneuralnetworks AT takisarwarsiddiqui earlydetectionofcervicalcancerusingdeepneuralnetworks |
| _version_ |
1814309296829104128 |