CancerCare: A reliable and secured self-supervising and interactive system using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2024
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10361-220592024-01-03T21:02:32Z CancerCare: A reliable and secured self-supervising and interactive system using deep learning Khisa, Pushpakali Hossain, Arisha Raihana, Anika Khan, Alisha Tonu, Tamanna Sultana Noor, Jannatun Department of Computer Science and Engineering, Brac University Histopathological cell image Deep convolutional neural network VGG16 VGG19 ResNet50 ResNet152 Xception DenseNet12 Survey Image recognition Oncologists Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-68). Cancer is the ultimate global health issue in the 21st century, as its burden is in creasing day by day. In the year 2020 [36], 18.1 million cancer cases were estimated, where 9.3 million were men and 8.8 million were women. Among these, many of the cases are detected at a very crucial stage due to a lack of advanced technologies to detect early symptoms, misinformation and ignorance. In recent years, the inno vation of many healthcare systems are capable of contributing to raising awareness and providing significant assistance to both oncologists to detect cancer disease and patients, which are progressively surging in popularity in the medical sector. How ever, [15] manual detection of cancer cells from the histopathological image is a very tiring, time-consuming process for histopathologists and many human errors can occur. Therefore, many computer-based detection processes have been invented, giving better results than the manual detection process. Although several archi tectures have been introduced, it becomes a question of which architecture gives us the best result for detecting cancer cells. In this proposed framework, we have analyzed five deep Convolutional Neural Network architectures such as VGG16, MobileNetV3, InceptionV3, Xception, and DenseNet121, which have been trained and tested on the lung cancer and colon cancer datasets, present the performance comparison between them and found out the best image recognition and classifica tion architecture which have given us the utmost accuracy for detecting any type of cancerous histopathological cell. Moreover, we have also designed a prototype for a user-friendly, self-supervising and reliable platform (“CancerCare” mobile ap plication) with some key features after conducting a survey in Bangladesh to make it easier for oncologists as well as patients to deal with this fatal disease. Besides, it also perpetuates smoother communication between patients and oncologists on a regular basis via live chat and video consultation. At present times, misuse of data in mHealth applications is one of the most noted risks. Therefore, we have established authentication using firebase. Pushpakali Khisa Arisha Hossain Anika Raihana Alisha Khan Tamanna Sultana Tonu B.Sc. in Computer Science 2024-01-03T08:04:37Z 2024-01-03T08:04:37Z 2023 2023-01 Thesis ID: 19101241 ID: 19101061 ID: 19101095 ID: 19101411 ID: 19101155 http://hdl.handle.net/10361/22059 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. 68 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Histopathological cell image Deep convolutional neural network VGG16 VGG19 ResNet50 ResNet152 Xception DenseNet12 Survey Image recognition Oncologists Cognitive learning theory (Deep learning) |
| spellingShingle |
Histopathological cell image Deep convolutional neural network VGG16 VGG19 ResNet50 ResNet152 Xception DenseNet12 Survey Image recognition Oncologists Cognitive learning theory (Deep learning) Khisa, Pushpakali Hossain, Arisha Raihana, Anika Khan, Alisha Tonu, Tamanna Sultana CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Noor, Jannatun |
| author_facet |
Noor, Jannatun Khisa, Pushpakali Hossain, Arisha Raihana, Anika Khan, Alisha Tonu, Tamanna Sultana |
| format |
Thesis |
| author |
Khisa, Pushpakali Hossain, Arisha Raihana, Anika Khan, Alisha Tonu, Tamanna Sultana |
| author_sort |
Khisa, Pushpakali |
| title |
CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| title_short |
CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| title_full |
CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| title_fullStr |
CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| title_full_unstemmed |
CancerCare: A reliable and secured self-supervising and interactive system using deep learning |
| title_sort |
cancercare: a reliable and secured self-supervising and interactive system using deep learning |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22059 |
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