An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5
With the advent of computer technology, Artificial Intelligence (AI) aids radiologists to diagnosis the Brain Tumor (BT). Early detection of diseases can be increased in health care leads to further treatments, wherein the typical application of AI systems performs a vital role in terms of time and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2151180 |
| _version_ | 1797641094468468736 |
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| author | Sivapathi Arunachalam Gopalakrishnan Sethumathavan |
| author_facet | Sivapathi Arunachalam Gopalakrishnan Sethumathavan |
| author_sort | Sivapathi Arunachalam |
| collection | DOAJ |
| description | With the advent of computer technology, Artificial Intelligence (AI) aids radiologists to diagnosis the Brain Tumor (BT). Early detection of diseases can be increased in health care leads to further treatments, wherein the typical application of AI systems performs a vital role in terms of time and money savings. Magnetic Resonance (MR) images are enhanced with image enhancement techniques to improve contrast and color accuracy. Besides, traditional methods uncompensated for problems with the several types of MR imaging for BT. Deep learning techniques can be extended to help overcome the common problems encountered in conventional tumor detection methods. Therefore, in this work, an improvised YOLOV5 technique have been proposed for BT detection based on MR images. Eventually, the idea of Hyperparameter Optimization (HPO) is applied using Hybrid Grid Search Optimizer Algorithm (HGSOA) to enhance the performance of the tumor detection viz tuning of hyper parameters in proposed deep neural network. To evaluate the effectiveness of proposed model, McCulloch’s algorithm is used to localize images for tumor region segmentation, and the segmentation result is also checked with truth annotated images. Various experiments were conducted to measure the accuracy of proposed fine-tuned model using MW brain test images. Finally, classification metrics including, MSE, PSNR, SSIM, FSIM, and CPU time are compared with existing state-of-the-art techniques to prove the effectiveness of the proposed model. In the taxonomy of MRI-BT, greater precision was achieved by CNN. |
| first_indexed | 2024-03-11T13:40:35Z |
| format | Article |
| id | doaj.art-a5e9cc59922943268e4de1f866a22dd1 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:35Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-a5e9cc59922943268e4de1f866a22dd12023-11-02T13:36:39ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.21511802151180An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5Sivapathi Arunachalam0Gopalakrishnan SethumathavanSASTRA Deemed UniversityWith the advent of computer technology, Artificial Intelligence (AI) aids radiologists to diagnosis the Brain Tumor (BT). Early detection of diseases can be increased in health care leads to further treatments, wherein the typical application of AI systems performs a vital role in terms of time and money savings. Magnetic Resonance (MR) images are enhanced with image enhancement techniques to improve contrast and color accuracy. Besides, traditional methods uncompensated for problems with the several types of MR imaging for BT. Deep learning techniques can be extended to help overcome the common problems encountered in conventional tumor detection methods. Therefore, in this work, an improvised YOLOV5 technique have been proposed for BT detection based on MR images. Eventually, the idea of Hyperparameter Optimization (HPO) is applied using Hybrid Grid Search Optimizer Algorithm (HGSOA) to enhance the performance of the tumor detection viz tuning of hyper parameters in proposed deep neural network. To evaluate the effectiveness of proposed model, McCulloch’s algorithm is used to localize images for tumor region segmentation, and the segmentation result is also checked with truth annotated images. Various experiments were conducted to measure the accuracy of proposed fine-tuned model using MW brain test images. Finally, classification metrics including, MSE, PSNR, SSIM, FSIM, and CPU time are compared with existing state-of-the-art techniques to prove the effectiveness of the proposed model. In the taxonomy of MRI-BT, greater precision was achieved by CNN.http://dx.doi.org/10.1080/08839514.2022.2151180 |
| spellingShingle | Sivapathi Arunachalam Gopalakrishnan Sethumathavan An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 Applied Artificial Intelligence |
| title | An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 |
| title_full | An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 |
| title_fullStr | An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 |
| title_full_unstemmed | An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 |
| title_short | An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5 |
| title_sort | effective tumor detection in mr brain images based on deep cnn approach i yolov5 |
| url | http://dx.doi.org/10.1080/08839514.2022.2151180 |
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