Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images
Kidney cancer is a commonly diagnosed cancer disease in recent years, and Renal Cell Carcinoma (RCC) is the most common kidney cancer responsible for 80% to 85% of all renal tumors. The diagnosis of kidney cancer requires manual examination and analysis of histopathological images of the affected ti...
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IEEE
2025-01-01
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| author | Amit Kumar Chanchal Shyam Lal Shilpa Suresh |
| author_facet | Amit Kumar Chanchal Shyam Lal Shilpa Suresh |
| author_sort | Amit Kumar Chanchal |
| collection | DOAJ |
| description | Kidney cancer is a commonly diagnosed cancer disease in recent years, and Renal Cell Carcinoma (RCC) is the most common kidney cancer responsible for 80% to 85% of all renal tumors. The diagnosis of kidney cancer requires manual examination and analysis of histopathological images of the affected tissue. This process is time-consuming, prone to human error, and highly depends on the expertise of a pathologist. Early detection and grading of kidney cancer tissues enable doctors and practitioners to decide the further course of treatment. Therefore, quick and precise analysis of kidney cancer tissue images is extremely important for proper diagnosis. Recently, deep learning algorithms have proved to be very efficient and accurate in histopathology image analysis. In this paper, we propose a computationally efficient deep-learning architecture based on convolutional neural networks (CNNs) to automate the grading and classification task for kidney cancer tissue. The proposed Robust CNN (RoCNN) architecture is capable of learning features at varying convolutional filter sizes because of the inception modules employed in it. Squeeze and Extract (SE) blocks are used to remove unnecessary contributions from noisy channels and improve model accuracy. Concatenating samples from three different parts of architecture allows for the encompassing of varied features, further improving grading and classification accuracy. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on two well-known datasets, the KMC kidney dataset of five different grades and the TCGA dataset of four classes. Compared to the best-performing state-of-the-art model the accuracy of RoCNN shows a significant improvement of about 4.22% and 3.01% for both datasets respectively. |
| format | Article |
| id | doaj-art-152276d59f114f1eb0a849ea9d28cf06 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-152276d59f114f1eb0a849ea9d28cf062025-08-20T03:51:29ZengIEEEIEEE Access2169-35362025-01-011312184912186710.1109/ACCESS.2025.358693511075668Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology ImagesAmit Kumar Chanchal0Shyam Lal1https://orcid.org/0000-0002-4355-6354Shilpa Suresh2https://orcid.org/0000-0003-1796-5995School of Computing, MIT Vishwaprayag University, Solapur, Maharashtra, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, IndiaDepartment of Mechatronics, Manipal Institute of Technology Manipal, Manipal Academy of Higher Education, Udupi, Karnataka, IndiaKidney cancer is a commonly diagnosed cancer disease in recent years, and Renal Cell Carcinoma (RCC) is the most common kidney cancer responsible for 80% to 85% of all renal tumors. The diagnosis of kidney cancer requires manual examination and analysis of histopathological images of the affected tissue. This process is time-consuming, prone to human error, and highly depends on the expertise of a pathologist. Early detection and grading of kidney cancer tissues enable doctors and practitioners to decide the further course of treatment. Therefore, quick and precise analysis of kidney cancer tissue images is extremely important for proper diagnosis. Recently, deep learning algorithms have proved to be very efficient and accurate in histopathology image analysis. In this paper, we propose a computationally efficient deep-learning architecture based on convolutional neural networks (CNNs) to automate the grading and classification task for kidney cancer tissue. The proposed Robust CNN (RoCNN) architecture is capable of learning features at varying convolutional filter sizes because of the inception modules employed in it. Squeeze and Extract (SE) blocks are used to remove unnecessary contributions from noisy channels and improve model accuracy. Concatenating samples from three different parts of architecture allows for the encompassing of varied features, further improving grading and classification accuracy. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on two well-known datasets, the KMC kidney dataset of five different grades and the TCGA dataset of four classes. Compared to the best-performing state-of-the-art model the accuracy of RoCNN shows a significant improvement of about 4.22% and 3.01% for both datasets respectively.https://ieeexplore.ieee.org/document/11075668/Kidney cancerdeep learningcancer detectionrenal cell carcinomacomputational intelligence |
| spellingShingle | Amit Kumar Chanchal Shyam Lal Shilpa Suresh Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images IEEE Access Kidney cancer deep learning cancer detection renal cell carcinoma computational intelligence |
| title | Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images |
| title_full | Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images |
| title_fullStr | Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images |
| title_full_unstemmed | Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images |
| title_short | Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images |
| title_sort | development of robust cnn architecture for grading and classification of renal cell carcinoma histology images |
| topic | Kidney cancer deep learning cancer detection renal cell carcinoma computational intelligence |
| url | https://ieeexplore.ieee.org/document/11075668/ |
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