Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large
Clinical image processing plays a significant role in healthcare systems and is a widely used methodology of the current era. The Intracranial tumor affects children and adults as it is the 10th most common form of tumor. Accurate tumor segmentation in the brain is complicated even though Intracrani...
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| Format: | Article |
| Language: | English |
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Springer
2022-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822001641 |
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| author | Mavra Mehmood Nasser Alshammari Saad Awadh Alanazi Asma Basharat Fahad Ahmad Muhammad Sajjad Kashaf Junaid |
| author_facet | Mavra Mehmood Nasser Alshammari Saad Awadh Alanazi Asma Basharat Fahad Ahmad Muhammad Sajjad Kashaf Junaid |
| author_sort | Mavra Mehmood |
| collection | DOAJ |
| description | Clinical image processing plays a significant role in healthcare systems and is a widely used methodology of the current era. The Intracranial tumor affects children and adults as it is the 10th most common form of tumor. Accurate tumor segmentation in the brain is complicated even though Intracranial tumor images are acquired properly. The tumor can be curable if its stage and form can be identified on time, and for this purpose, researchers have been developing sophisticated techniques and methods. An automatic detection, segmentation, colorization, and classification of tumor region are done to identify abnormalities in a medical image using T1-CE, an MRI dataset that assists in diagnosis. To assist the medical practitioners in visualizing tumor shape, size, and orientation; only the tumor region is colored in a greyscale image. Pix2Pix Conditional Generative Adversarial Neural Networks (Pix2Pix-cGANs) have generated MRI images with color tumor regions. In qualitative measures, we achieved the Structure Similarity Index (SSIM) average score of 0.92% and an average 28% Peak Signal to Noise Ratio (PSNR) value on generated images that outperform the existing techniques. Moreover, we achieved Classification Accuracy (CA) 88.5% and 92.4% quantitatively in pre and post colorization phases, respectively, with other measures using NASNet-Large. |
| format | Article |
| id | doaj-art-a614acaf0ed44666987bf2befb834ce0 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-a614acaf0ed44666987bf2befb834ce02025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-07-013474358437410.1016/j.jksuci.2022.05.015Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-largeMavra Mehmood0Nasser Alshammari1Saad Awadh Alanazi2Asma Basharat3Fahad Ahmad4Muhammad Sajjad5Kashaf Junaid6Department of Computer Sciences, Kinnaird College for Women, Lahore, Punjab 54700, PakistanDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, AlJouf 72388, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, AlJouf 72388, Saudi ArabiaDepartment of Computer Sciences, Forman Christian College, Lahore, Punjab 54700, Pakistan; School of Computing, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, MalaysiaDepartment of Basic Sciences, Deanship of Common First Year, Jouf University, Sakaka, AlJouf 72341, Saudi Arabia; Corresponding author.Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, Punjab 54000, PakistanDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka, AlJouf 72388, Saudi ArabiaClinical image processing plays a significant role in healthcare systems and is a widely used methodology of the current era. The Intracranial tumor affects children and adults as it is the 10th most common form of tumor. Accurate tumor segmentation in the brain is complicated even though Intracranial tumor images are acquired properly. The tumor can be curable if its stage and form can be identified on time, and for this purpose, researchers have been developing sophisticated techniques and methods. An automatic detection, segmentation, colorization, and classification of tumor region are done to identify abnormalities in a medical image using T1-CE, an MRI dataset that assists in diagnosis. To assist the medical practitioners in visualizing tumor shape, size, and orientation; only the tumor region is colored in a greyscale image. Pix2Pix Conditional Generative Adversarial Neural Networks (Pix2Pix-cGANs) have generated MRI images with color tumor regions. In qualitative measures, we achieved the Structure Similarity Index (SSIM) average score of 0.92% and an average 28% Peak Signal to Noise Ratio (PSNR) value on generated images that outperform the existing techniques. Moreover, we achieved Classification Accuracy (CA) 88.5% and 92.4% quantitatively in pre and post colorization phases, respectively, with other measures using NASNet-Large.http://www.sciencedirect.com/science/article/pii/S1319157822001641Medical imagingIntracranial tumorMagnetic resonance imagingDetectionSegmentationColorization |
| spellingShingle | Mavra Mehmood Nasser Alshammari Saad Awadh Alanazi Asma Basharat Fahad Ahmad Muhammad Sajjad Kashaf Junaid Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large Journal of King Saud University: Computer and Information Sciences Medical imaging Intracranial tumor Magnetic resonance imaging Detection Segmentation Colorization |
| title | Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large |
| title_full | Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large |
| title_fullStr | Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large |
| title_full_unstemmed | Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large |
| title_short | Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large |
| title_sort | improved colorization and classification of intracranial tumor expanse in mri images via hybrid scheme of pix2pix cgans and nasnet large |
| topic | Medical imaging Intracranial tumor Magnetic resonance imaging Detection Segmentation Colorization |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822001641 |
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