Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model
Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. T...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/19/3152 |
| _version_ | 1797576019439255552 |
|---|---|
| author | Gurjinder Kaur Meenu Garg Sheifali Gupta Sapna Juneja Junaid Rashid Deepali Gupta Asadullah Shah Asadullah Shaikh |
| author_facet | Gurjinder Kaur Meenu Garg Sheifali Gupta Sapna Juneja Junaid Rashid Deepali Gupta Asadullah Shah Asadullah Shaikh |
| author_sort | Gurjinder Kaur |
| collection | DOAJ |
| description | Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model’s capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model’s superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches. |
| first_indexed | 2024-03-10T21:46:35Z |
| format | Article |
| id | doaj.art-5a576e15cdb547d99509a8ea8dc7aea1 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T21:46:35Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-5a576e15cdb547d99509a8ea8dc7aea12023-11-19T14:15:31ZengMDPI AGDiagnostics2075-44182023-10-011319315210.3390/diagnostics13193152Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet ModelGurjinder Kaur0Meenu Garg1Sheifali Gupta2Sapna Juneja3Junaid Rashid4Deepali Gupta5Asadullah Shah6Asadullah Shaikh7Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaKulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaDepartment of Data Science, Sejong University, Seoul 05006, Republic of KoreaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaKulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi ArabiaGlomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model’s capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model’s superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.https://www.mdpi.com/2075-4418/13/19/3152deep learningdetectionglomerularkidney tissueUNetwhole-slide images |
| spellingShingle | Gurjinder Kaur Meenu Garg Sheifali Gupta Sapna Juneja Junaid Rashid Deepali Gupta Asadullah Shah Asadullah Shaikh Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model Diagnostics deep learning detection glomerular kidney tissue UNet whole-slide images |
| title | Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model |
| title_full | Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model |
| title_fullStr | Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model |
| title_full_unstemmed | Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model |
| title_short | Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model |
| title_sort | automatic identification of glomerular in whole slide images using a modified unet model |
| topic | deep learning detection glomerular kidney tissue UNet whole-slide images |
| url | https://www.mdpi.com/2075-4418/13/19/3152 |
| work_keys_str_mv | AT gurjinderkaur automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT meenugarg automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT sheifaligupta automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT sapnajuneja automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT junaidrashid automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT deepaligupta automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT asadullahshah automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel AT asadullahshaikh automaticidentificationofglomerularinwholeslideimagesusingamodifiedunetmodel |