Document Details
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Document Type |
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Thesis |
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Document Title |
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Deep Learning Assessment on Detecting Colon Cancer under Different Image Types تقييم التعلم العميق في الكشف عن سرطان القولون ضمن انواع صور مختلفة |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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Colon cancer is considered one of the cancers with high incidence and mortality rates. Statistics indicate that colon cancer ranks second and third in death and incidence, respectively. Colon cancer begins as polyps that grow on the colon’s wall in the large intestine and may spread to other body areas over time. The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. The experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model, outperforms all the other models by generating the highest average performance result of 99.1%, 99.1%, 99.8%, and 99.1% based on the accuracy, AUC, precision, and F1, respectively. |
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Supervisor |
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Dr. Turki Talal Turki |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1445 AH
2023 AD |
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Co-Supervisor |
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Dr. Khalid Ateatallah Alsubhi |
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Added Date |
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Wednesday, November 1, 2023 |
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Researchers
| وائل حميدان الحازمي | Alhazmi, Wael Humaidan | Researcher | Master | |
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