Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset
Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual and labor-intensive. Du...
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MDPI AG
2025-02-01
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| Series: | Applied Biosciences |
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| author | Muskan Naresh Jain Salah Mohammed Awad Al-Heejawi Jamil R. Azzi Saeed Amal |
| author_facet | Muskan Naresh Jain Salah Mohammed Awad Al-Heejawi Jamil R. Azzi Saeed Amal |
| author_sort | Muskan Naresh Jain |
| collection | DOAJ |
| description | Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual and labor-intensive. Due to this issue, many are interested in computer-aided diagnostic technologies to assist pathologists in their diagnostics. Specifically, deep learning (DL) has become a viable remedy in this field. Nonetheless, the capacity of existing DL models to extract comprehensive visual features for accurate classification is limited. Toward the end, this study proposes using ensemble models that combine the strengths of multiple transformers and deep learning model architectures. By leveraging the collective knowledge of these models, the ensemble enhances classification performance and enables more precise and effective kidney cancer detection. This study compares the performance of these suggested models to previous studies, all of which used the publicly accessible Dartmouth Kidney Cancer Histology Dataset. This study showed that the Vision Transformers, with an average accuracy of over 99%, were able to achieve high detection accuracy across all complete slide picture patches. In particular, the CAiT, DeiT, ViT, and Swin models outperformed ResNet. All things considered, the Vision Transformers consistently produced an average accuracy of 98.51% across all five-folds. These results demonstrated that Vision Transformers might perform well and successfully identify important features from smaller patches. Through utilizing histopathological images, our findings will assist pathologists in diagnosing kidney cancer, resulting in early detection and increased patient survival rates. |
| format | Article |
| id | doaj-art-ea06307bf47a4584bffac92dcbfc4901 |
| institution | Kabale University |
| issn | 2813-0464 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Applied Biosciences |
| spelling | doaj-art-ea06307bf47a4584bffac92dcbfc49012025-08-20T03:43:51ZengMDPI AGApplied Biosciences2813-04642025-02-0141810.3390/applbiosci4010008Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology DatasetMuskan Naresh Jain0Salah Mohammed Awad Al-Heejawi1Jamil R. Azzi2Saeed Amal3College of Engineering, Northeastern University, Boston, MA 02115, USACollege of Engineering, Northeastern University, Boston, MA 02115, USATransplantation Research Center, Renal and Engineering Divisions, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USAThe Roux Institute, Northeastern University, Portland, ME 04101, USAKidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual and labor-intensive. Due to this issue, many are interested in computer-aided diagnostic technologies to assist pathologists in their diagnostics. Specifically, deep learning (DL) has become a viable remedy in this field. Nonetheless, the capacity of existing DL models to extract comprehensive visual features for accurate classification is limited. Toward the end, this study proposes using ensemble models that combine the strengths of multiple transformers and deep learning model architectures. By leveraging the collective knowledge of these models, the ensemble enhances classification performance and enables more precise and effective kidney cancer detection. This study compares the performance of these suggested models to previous studies, all of which used the publicly accessible Dartmouth Kidney Cancer Histology Dataset. This study showed that the Vision Transformers, with an average accuracy of over 99%, were able to achieve high detection accuracy across all complete slide picture patches. In particular, the CAiT, DeiT, ViT, and Swin models outperformed ResNet. All things considered, the Vision Transformers consistently produced an average accuracy of 98.51% across all five-folds. These results demonstrated that Vision Transformers might perform well and successfully identify important features from smaller patches. Through utilizing histopathological images, our findings will assist pathologists in diagnosing kidney cancer, resulting in early detection and increased patient survival rates.https://www.mdpi.com/2813-0464/4/1/8kidney cancer diagnosisdeep learningconvolutional neural networksimage classificationartificial intelligencecomputer vision |
| spellingShingle | Muskan Naresh Jain Salah Mohammed Awad Al-Heejawi Jamil R. Azzi Saeed Amal Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset Applied Biosciences kidney cancer diagnosis deep learning convolutional neural networks image classification artificial intelligence computer vision |
| title | Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset |
| title_full | Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset |
| title_fullStr | Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset |
| title_full_unstemmed | Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset |
| title_short | Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset |
| title_sort | digital pathology and ensemble deep learning for kidney cancer diagnosis dartmouth kidney cancer histology dataset |
| topic | kidney cancer diagnosis deep learning convolutional neural networks image classification artificial intelligence computer vision |
| url | https://www.mdpi.com/2813-0464/4/1/8 |
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