Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning
Kidney renal carcinoma is a type of cancer that originates in the renal cortex, the outer part of the kidney. It includes various subtypes, such as clear cell, papillary and chromophobe renal cell carcinomas, which are characterized by different cellular structures and behaviours. This cancer is oft...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10711182/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850210455331536896 |
|---|---|
| author | Zaib Akram Kashif Munir Muhammad Usama Tanveer Atiq Ur Rehman Amine Bermak |
| author_facet | Zaib Akram Kashif Munir Muhammad Usama Tanveer Atiq Ur Rehman Amine Bermak |
| author_sort | Zaib Akram |
| collection | DOAJ |
| description | Kidney renal carcinoma is a type of cancer that originates in the renal cortex, the outer part of the kidney. It includes various subtypes, such as clear cell, papillary and chromophobe renal cell carcinomas, which are characterized by different cellular structures and behaviours. This cancer is often detected through imaging techniques and poses significant challenges due to its potential to metastasize and vary in treatment response. To address these challenges, we developed a novel computational framework named Kidney Ensemble-Net, designed to enhance the accuracy of renal carcinoma classification. Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. These extracted features are then transferred into a refined probabilistic feature set, upon which we construct an ensemble model leveraging the strengths of Logistic Regression (LR), Random Forest (RF), and Gaussian Naive Bayes (GNB) classifiers. The integration of these models within the Kidney Ensemble-Net architecture resulted in an outstanding performance, with our Kidney Ensemble-Net + LR model achieving a 99.72% accuracy score significantly surpassing existing state-of-the-art methodologies. Furthermore, we rigorously evaluated our model using k-fold validation analysis, ensuring its robustness and generalizability across diverse datasets. This comprehensive comparison with current leading approaches highlights the potential of Kidney Ensemble-Net as a powerful tool for the precise and reliable classification of kidney renal carcinoma, paving the way for improved diagnostic and treatment strategies. |
| format | Article |
| id | doaj-art-34c7d588cf3e4cc8957f3de208accf55 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-34c7d588cf3e4cc8957f3de208accf552025-08-20T02:09:47ZengIEEEIEEE Access2169-35362024-01-011215067915069210.1109/ACCESS.2024.347649310711182Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble LearningZaib Akram0https://orcid.org/0009-0004-5605-2608Kashif Munir1https://orcid.org/0000-0001-5114-4213Muhammad Usama Tanveer2https://orcid.org/0009-0002-7374-9461Atiq Ur Rehman3https://orcid.org/0000-0003-0248-7919Amine Bermak4https://orcid.org/0000-0003-4984-6093Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanInstitute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanInstitute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarKidney renal carcinoma is a type of cancer that originates in the renal cortex, the outer part of the kidney. It includes various subtypes, such as clear cell, papillary and chromophobe renal cell carcinomas, which are characterized by different cellular structures and behaviours. This cancer is often detected through imaging techniques and poses significant challenges due to its potential to metastasize and vary in treatment response. To address these challenges, we developed a novel computational framework named Kidney Ensemble-Net, designed to enhance the accuracy of renal carcinoma classification. Our approach begins by acquiring spatial features from contrast-enhanced images using a Convolutional Neural Network (CNN) effectively capturing intricate patterns and structures characteristic of different carcinoma subtypes. These extracted features are then transferred into a refined probabilistic feature set, upon which we construct an ensemble model leveraging the strengths of Logistic Regression (LR), Random Forest (RF), and Gaussian Naive Bayes (GNB) classifiers. The integration of these models within the Kidney Ensemble-Net architecture resulted in an outstanding performance, with our Kidney Ensemble-Net + LR model achieving a 99.72% accuracy score significantly surpassing existing state-of-the-art methodologies. Furthermore, we rigorously evaluated our model using k-fold validation analysis, ensuring its robustness and generalizability across diverse datasets. This comprehensive comparison with current leading approaches highlights the potential of Kidney Ensemble-Net as a powerful tool for the precise and reliable classification of kidney renal carcinoma, paving the way for improved diagnostic and treatment strategies.https://ieeexplore.ieee.org/document/10711182/Renal cell carcinoma (RCC)Kidney Ensemble-Netprobabilistic featuresmachine learning |
| spellingShingle | Zaib Akram Kashif Munir Muhammad Usama Tanveer Atiq Ur Rehman Amine Bermak Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning IEEE Access Renal cell carcinoma (RCC) Kidney Ensemble-Net probabilistic features machine learning |
| title | Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning |
| title_full | Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning |
| title_fullStr | Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning |
| title_full_unstemmed | Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning |
| title_short | Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning |
| title_sort | kidney ensemble net enhancing renal carcinoma detection through probabilistic feature selection and ensemble learning |
| topic | Renal cell carcinoma (RCC) Kidney Ensemble-Net probabilistic features machine learning |
| url | https://ieeexplore.ieee.org/document/10711182/ |
| work_keys_str_mv | AT zaibakram kidneyensemblenetenhancingrenalcarcinomadetectionthroughprobabilisticfeatureselectionandensemblelearning AT kashifmunir kidneyensemblenetenhancingrenalcarcinomadetectionthroughprobabilisticfeatureselectionandensemblelearning AT muhammadusamatanveer kidneyensemblenetenhancingrenalcarcinomadetectionthroughprobabilisticfeatureselectionandensemblelearning AT atiqurrehman kidneyensemblenetenhancingrenalcarcinomadetectionthroughprobabilisticfeatureselectionandensemblelearning AT aminebermak kidneyensemblenetenhancingrenalcarcinomadetectionthroughprobabilisticfeatureselectionandensemblelearning |