CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm
This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, compa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7703 |
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| author | Abror Shavkatovich Buriboev Ahmadjon Khashimov Akmal Abduvaitov Heung Seok Jeon |
| author_facet | Abror Shavkatovich Buriboev Ahmadjon Khashimov Akmal Abduvaitov Heung Seok Jeon |
| author_sort | Abror Shavkatovich Buriboev |
| collection | DOAJ |
| description | This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, comparing the original, standard CLAHE and modified CLAHE versions of the dataset. The BRISQUE score decreased from 28.8 in the original dataset to 21.1 with the modified CLAHE method, indicating a significant improvement in image quality. Furthermore, CNN segmentation accuracy rose from 0.951 with the original dataset to 0.996 with the modified CLAHE method, outperforming the accuracy achieved with standard CLAHE preprocessing (0.969). These results highlight the benefits of the modified CLAHE method in refining image quality and enhancing segmentation performance. This study highlights the value of adaptive preprocessing in medical imaging workflows and shows that CNN-based kidney segmentation accuracy may be greatly increased by altering conventional CLAHE. Our method provides insightful information on optimizing preprocessing for medical imaging applications, leading to more accurate and dependable segmentation results for better clinical diagnosis. |
| format | Article |
| id | doaj-art-dc2487cb2981402b8f209f12e05ccd7a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-dc2487cb2981402b8f209f12e05ccd7a2024-12-13T16:32:29ZengMDPI AGSensors1424-82202024-12-012423770310.3390/s24237703CNN-Based Kidney Segmentation Using a Modified CLAHE AlgorithmAbror Shavkatovich Buriboev0Ahmadjon Khashimov1Akmal Abduvaitov2Heung Seok Jeon3Department of AI-Software, Gachon University, Seongnam-si 13120, Republic of KoreaDepartment of Digital Technologies and Mathematics, Kokand University, Kokand 150700, UzbekistanDepartment of IT, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 100084, UzbekistanDepartment of Computer Engineering, Konkuk University, Chungju 27478, Republic of KoreaThis paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, comparing the original, standard CLAHE and modified CLAHE versions of the dataset. The BRISQUE score decreased from 28.8 in the original dataset to 21.1 with the modified CLAHE method, indicating a significant improvement in image quality. Furthermore, CNN segmentation accuracy rose from 0.951 with the original dataset to 0.996 with the modified CLAHE method, outperforming the accuracy achieved with standard CLAHE preprocessing (0.969). These results highlight the benefits of the modified CLAHE method in refining image quality and enhancing segmentation performance. This study highlights the value of adaptive preprocessing in medical imaging workflows and shows that CNN-based kidney segmentation accuracy may be greatly increased by altering conventional CLAHE. Our method provides insightful information on optimizing preprocessing for medical imaging applications, leading to more accurate and dependable segmentation results for better clinical diagnosis.https://www.mdpi.com/1424-8220/24/23/7703kidney segmentationCNNimage enhancement |
| spellingShingle | Abror Shavkatovich Buriboev Ahmadjon Khashimov Akmal Abduvaitov Heung Seok Jeon CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm Sensors kidney segmentation CNN image enhancement |
| title | CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm |
| title_full | CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm |
| title_fullStr | CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm |
| title_full_unstemmed | CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm |
| title_short | CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm |
| title_sort | cnn based kidney segmentation using a modified clahe algorithm |
| topic | kidney segmentation CNN image enhancement |
| url | https://www.mdpi.com/1424-8220/24/23/7703 |
| work_keys_str_mv | AT abrorshavkatovichburiboev cnnbasedkidneysegmentationusingamodifiedclahealgorithm AT ahmadjonkhashimov cnnbasedkidneysegmentationusingamodifiedclahealgorithm AT akmalabduvaitov cnnbasedkidneysegmentationusingamodifiedclahealgorithm AT heungseokjeon cnnbasedkidneysegmentationusingamodifiedclahealgorithm |