Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer
Abstract Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomogra...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-025-01578-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861512791392256 |
|---|---|
| author | Sandeep Dwarkanth Pande Pala Kalyani S Nagendram Ala Saleh Alluhaidan G Harish Babu Sk Hasane Ahammad Vivek Kumar Pandey G Sridevi Abhinav Kumar Ebenezer Bonyah |
| author_facet | Sandeep Dwarkanth Pande Pala Kalyani S Nagendram Ala Saleh Alluhaidan G Harish Babu Sk Hasane Ahammad Vivek Kumar Pandey G Sridevi Abhinav Kumar Ebenezer Bonyah |
| author_sort | Sandeep Dwarkanth Pande |
| collection | DOAJ |
| description | Abstract Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. The contribution includes enhancing the edges and textures of CT images through filtering to achieve precise liver segmentation. Additionally, an existing DL framework was employed for liver cancer detection and segmentation. The strengths of this paper include a clear emphasis on the criticality of liver cancer detection in biomedical imaging and diagnostics. It also highlights the challenges associated with CT image detection and segmentation and provides a comprehensive summary of recent literature. However, certain difficulties arise during the detection process in CT images due to overlapping structures, such as bile ducts, blood vessels, image noise, textural changes, size and location variations, and inherent heterogeneity. These factors may lead to segmentation errors and subsequently different analyses. This research analysis compares two advanced methodologies, DCNN and HFCNN, for liver cancer detection. The evaluation of DCNN and HFCNN in liver cancer detection is conducted using multiple performance metrics, including precision, F1-score, recall, and accuracy. This comprehensive assessment provides a detailed evaluation of these models’ effectiveness compared to other state-of-the-art methods in identifying liver cancer. |
| format | Article |
| id | doaj-art-e355d7f9838a4d309292743deab34e1d |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-e355d7f9838a4d309292743deab34e1d2025-02-09T12:59:57ZengBMCBMC Medical Imaging1471-23422025-02-0125111410.1186/s12880-025-01578-4Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancerSandeep Dwarkanth Pande0Pala Kalyani1S Nagendram2Ala Saleh Alluhaidan3G Harish Babu4Sk Hasane Ahammad5Vivek Kumar Pandey6G Sridevi7Abhinav Kumar8Ebenezer Bonyah9MIT, academy of EngineeringDepartment of ECE, Vardhaman college of EngineeringDepartment of AI, KKR & KSR Institute of Technology & SciencesDepartment of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman UniversityDepartment of ECE, CVR College of EngineeringDepartment of ECE, Koneru Lakshmaiah Education FoundationCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Computer Science and Engineering, Raghu Engineering CollegeDepartment of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris YeltsinDepartment of Mathematics Education, Akenten Appiah Menka University of Skills Training and Entrepreneurial DevelopmentAbstract Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. The contribution includes enhancing the edges and textures of CT images through filtering to achieve precise liver segmentation. Additionally, an existing DL framework was employed for liver cancer detection and segmentation. The strengths of this paper include a clear emphasis on the criticality of liver cancer detection in biomedical imaging and diagnostics. It also highlights the challenges associated with CT image detection and segmentation and provides a comprehensive summary of recent literature. However, certain difficulties arise during the detection process in CT images due to overlapping structures, such as bile ducts, blood vessels, image noise, textural changes, size and location variations, and inherent heterogeneity. These factors may lead to segmentation errors and subsequently different analyses. This research analysis compares two advanced methodologies, DCNN and HFCNN, for liver cancer detection. The evaluation of DCNN and HFCNN in liver cancer detection is conducted using multiple performance metrics, including precision, F1-score, recall, and accuracy. This comprehensive assessment provides a detailed evaluation of these models’ effectiveness compared to other state-of-the-art methods in identifying liver cancer.https://doi.org/10.1186/s12880-025-01578-4Liver cancerSegmentationDiagnosisBiomedical image processingDetection |
| spellingShingle | Sandeep Dwarkanth Pande Pala Kalyani S Nagendram Ala Saleh Alluhaidan G Harish Babu Sk Hasane Ahammad Vivek Kumar Pandey G Sridevi Abhinav Kumar Ebenezer Bonyah Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer BMC Medical Imaging Liver cancer Segmentation Diagnosis Biomedical image processing Detection |
| title | Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer |
| title_full | Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer |
| title_fullStr | Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer |
| title_full_unstemmed | Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer |
| title_short | Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer |
| title_sort | comparative analysis of the dcnn and hfcnn based computerized detection of liver cancer |
| topic | Liver cancer Segmentation Diagnosis Biomedical image processing Detection |
| url | https://doi.org/10.1186/s12880-025-01578-4 |
| work_keys_str_mv | AT sandeepdwarkanthpande comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT palakalyani comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT snagendram comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT alasalehalluhaidan comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT gharishbabu comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT skhasaneahammad comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT vivekkumarpandey comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT gsridevi comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT abhinavkumar comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer AT ebenezerbonyah comparativeanalysisofthedcnnandhfcnnbasedcomputerizeddetectionoflivercancer |