Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach
This study introduces a novel Black Widow-Optimized Dense CNN (BW-DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes c...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/1622294 |
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| author | Sangeeta Kakarwal Rahul Mapari Anuj Kumar Jain Vipin Sharma Pranoti Prashant Mane Jambi Ratna Raja Kumar Ranjan K. Pradhan |
| author_facet | Sangeeta Kakarwal Rahul Mapari Anuj Kumar Jain Vipin Sharma Pranoti Prashant Mane Jambi Ratna Raja Kumar Ranjan K. Pradhan |
| author_sort | Sangeeta Kakarwal |
| collection | DOAJ |
| description | This study introduces a novel Black Widow-Optimized Dense CNN (BW-DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time-consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW-DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW-DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW-DCNN approach to significantly advance the capabilities of computer-aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings. |
| format | Article |
| id | doaj-art-c4fd7cfbf9fe4cf7888d0807c3f56a49 |
| institution | DOAJ |
| issn | 2314-4912 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-c4fd7cfbf9fe4cf7888d0807c3f56a492025-08-20T03:04:40ZengWileyJournal of Engineering2314-49122024-01-01202410.1155/2024/1622294Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN ApproachSangeeta Kakarwal0Rahul Mapari1Anuj Kumar Jain2Vipin Sharma3Pranoti Prashant Mane4Jambi Ratna Raja Kumar5Ranjan K. Pradhan6International Centre of Excellence in Engineering and ManagementDr. Babasaheb Ambedkar Marathwada UniversityDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringMES’s Wadia College of EngineeringComputer Engineering DepartmentDepartment of BiotechnologyThis study introduces a novel Black Widow-Optimized Dense CNN (BW-DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time-consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW-DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW-DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW-DCNN approach to significantly advance the capabilities of computer-aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings.http://dx.doi.org/10.1155/2024/1622294 |
| spellingShingle | Sangeeta Kakarwal Rahul Mapari Anuj Kumar Jain Vipin Sharma Pranoti Prashant Mane Jambi Ratna Raja Kumar Ranjan K. Pradhan Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach Journal of Engineering |
| title | Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach |
| title_full | Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach |
| title_fullStr | Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach |
| title_full_unstemmed | Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach |
| title_short | Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach |
| title_sort | enhanced detection and segmentation of brain tumors using a dense bw cnn approach |
| url | http://dx.doi.org/10.1155/2024/1622294 |
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