Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models
Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/14/5429 |
| _version_ | 1797443795304841216 |
|---|---|
| author | Esam Othman Muhammad Mahmoud Habib Dhahri Hatem Abdulkader Awais Mahmood Mina Ibrahim |
| author_facet | Esam Othman Muhammad Mahmoud Habib Dhahri Hatem Abdulkader Awais Mahmood Mina Ibrahim |
| author_sort | Esam Othman |
| collection | DOAJ |
| description | Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year. |
| first_indexed | 2024-03-09T13:03:10Z |
| format | Article |
| id | doaj.art-cc032e84c7da47aa9153994183a76fb6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:10Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cc032e84c7da47aa9153994183a76fb62023-11-30T21:52:46ZengMDPI AGSensors1424-82202022-07-012214542910.3390/s22145429Automatic Detection of Liver Cancer Using Hybrid Pre-Trained ModelsEsam Othman0Muhammad Mahmoud1Habib Dhahri2Hatem Abdulkader3Awais Mahmood4Mina Ibrahim5Faculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Systems, Madina Higher Institute of Management and Technology, Shabramant 12947, EgyptFaculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Systems, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, EgyptFaculty of Applied Computer Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom 32511, Menoufia, EgyptLiver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.https://www.mdpi.com/1424-8220/22/14/5429CNNliver cancerCT imagesdeep learningpre-trained models |
| spellingShingle | Esam Othman Muhammad Mahmoud Habib Dhahri Hatem Abdulkader Awais Mahmood Mina Ibrahim Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models Sensors CNN liver cancer CT images deep learning pre-trained models |
| title | Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models |
| title_full | Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models |
| title_fullStr | Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models |
| title_full_unstemmed | Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models |
| title_short | Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models |
| title_sort | automatic detection of liver cancer using hybrid pre trained models |
| topic | CNN liver cancer CT images deep learning pre-trained models |
| url | https://www.mdpi.com/1424-8220/22/14/5429 |
| work_keys_str_mv | AT esamothman automaticdetectionoflivercancerusinghybridpretrainedmodels AT muhammadmahmoud automaticdetectionoflivercancerusinghybridpretrainedmodels AT habibdhahri automaticdetectionoflivercancerusinghybridpretrainedmodels AT hatemabdulkader automaticdetectionoflivercancerusinghybridpretrainedmodels AT awaismahmood automaticdetectionoflivercancerusinghybridpretrainedmodels AT minaibrahim automaticdetectionoflivercancerusinghybridpretrainedmodels |