Deep Learning Model-based Decision Support System for Kidney Cancer on Renal Images
Received: 8 July 2024 | Revised: 7 August 2024 | Accepted: 11 August 2024 | Online: 31 August 2024
Corresponding author: Ibraheem Kasim Ibraheem
Abstract
Kidney cancer comes in various forms. Renal Cell Carcinoma (RCC) is the most severe and common kind of kidney cancer. Earlier diagnosis of kidney cancer has enormous advantages in implementing preventive measures to reduce its effects and death rates and overcome the tumor. Manually detecting Whole Slide Images (WSI) of renal tissues is a basic approach to predicting and diagnosing RCC. However, manual analysis of RCC is prone to inter-subject variability and is time-consuming. Compared to time-consuming and tedious classical diagnostic methods, automatic Deep Learning (DL) detection algorithms can improve test accuracy and reduce diagnostic time, radiologist workload, and costs. The study presents a Computational Intelligence with a Deep Learning Decision Support System for Kidney Cancer (CIDL-DSSKC) technique on renal images. The CIDL-DSSKC model analyzes renal images to identify and classify kidney cancer. The proposed method uses Median and Wiener filters for image preprocessing and the Xception model to derive a useful set of feature vectors. In addition, the Flower Pollination Algorithm (FPA) is employed to optimally choose parameters for the Xception method. The
Keywords:
computational intelligence, nature-inspired algorithm, deep learning, decision support system, kidney cancerDownloads
References
F. Hao, X. Liu, M. Li, and W. Han, "Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy," Life, vol. 13, no. 2, Feb. 2023, Art. no. 399.
J. Noorbakhsh et al., "Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images," Nature Communications, vol. 11, no. 1, Dec. 2020, Art. no. 6367.
E. Marostica et al., "Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma," Clinical Cancer Research, vol. 27, no. 10, pp. 2868–2878, May 2021.
S. Tabibu, P. K. Vinod, and C. V. Jawahar, "Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning," Scientific Reports, vol. 9, no. 1, Jul. 2019, Art. no. 10509.
E. Wulczyn et al., "Deep learning-based survival prediction for multiple cancer types using histopathology images," PLOS ONE, vol. 15, no. 6, 2020, Art. no. e0233678.
Y. Jiao, J. Li, C. Qian, and S. Fei, "Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images," Computer Methods and Programs in Biomedicine, vol. 204, Jun. 2021, Art. no. 106047.
H. J. Jang, A. Lee, J. Kang, I. H. Song, and S. H. Lee, "Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning," World Journal of Gastroenterology, vol. 26, no. 40, pp. 6207–6223, Oct. 2020.
J. Kers et al., "Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study," The Lancet Digital Health, vol. 4, no. 1, pp. e18–e26, Jan. 2022.
M. H. Almusawy, "Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control for Intelligent Transportation Systems in Smart Cities," Journal of Smart Internet of Things, vol. 2022, no. 1, pp. 81–96, Dec. 2022.
A. Sinha, P. Jha, B. Kumar, A. Mishra, V. Ujjwal, and A. Singh, "Blockchain-Based Smart Home Network Security through ML," Journal of Smart Internet of Things, vol. 2022, no. 1, pp. 1–9, Dec. 2022.
W. Alkaberi and F. Assiri, "Predicting the Number of Software Faults using Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13222–13231, Apr. 2024.
F. Lin et al., "A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma," European Journal of Radiology, vol. 129, Aug. 2020, Art. no. 109079.
C. Ohe et al., "Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma," Human Pathology, vol. 131, pp. 68–78, Jan. 2023.
A. A. Najm, I. K. Ibraheem, A. T. Azar, and A. J. Humaidi, "Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System," Sensors, vol. 20, no. 12, Jan. 2020, Art. no. 3576.
E. Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal blood vessel segmentation using bee colony optimisation and pattern search," in 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, Jul. 2014, pp. 1001–1006.
T. Ashfaq et al., "A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism," Sensors, vol. 22, no. 19, Jan. 2022, Art. no. 7162.
A. Koubaa, A. Ammar, M. Alahdab, A. Kanhouch, and A. T. Azar, "DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications," Sensors, vol. 20, no. 18, Jan. 2020, Art. no. 5240.
A. K. Chanchal, A. Kumar, S. Lal, and J. Kini, "Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images," Computers & Electrical Engineering, vol. 92, Jun. 2021, Art. no. 107177.
L. Zhou, Z. Zhang, Y.-C. Chen, Z.-Y. Zhao, X.-D. Yin, and H.-B. Jiang, "A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors," Translational Oncology, vol. 12, no. 2, pp. 292–300, Feb. 2019.
M. Zhu, B. Ren, R. Richards, M. Suriawinata, N. Tomita, and S. Hassanpour, "Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides," Scientific Reports, vol. 11, no. 1, Mar. 2021, Art. no. 7080.
H. A. Abdeltawab, F. A. Khalifa, M. A. Ghazal, L. Cheng, A. S. El-Baz, and D. D. Gondim, "A deep learning framework for automated classification of histopathological kidney whole-slide images," Journal of Pathology Informatics, vol. 13, Jan. 2022, Art. no. 100093.
S. Schulz et al., "Multimodal Deep Learning for Prognosis Prediction in Renal Cancer," Frontiers in Oncology, vol. 11, Nov. 2021.
K. H. Uhm et al., "Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography," npj Precision Oncology, vol. 5, no. 1, pp. 1–6, Jun. 2021.
A. A. Aatresh et al., "Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images," Computerized Medical Imaging and Graphics, vol. 93, Oct. 2021, Art. no. 101975.
J. DiPalma, A. A. Suriawinata, L. J. Tafe, L. Torresani, and S. Hassanpour, "Resolution-based distillation for efficient histology image classification," Artificial Intelligence in Medicine, vol. 119, Sep. 2021, Art. no. 102136.
"CT Kidney Dataset: Normal-Cyst-Tumor and Stone." https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone.
C. R. Park, S. H. Kang, and Y. Lee, "Median modified wiener filter for improving the image quality of gamma camera images," Nuclear Engineering and Technology, vol. 52, no. 10, pp. 2328–2333, Oct. 2020.
F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 1800–1807.
Z. A. Alkareem Alyasseri et al., "A hybrid flower pollination with β-hill climbing algorithm for global optimization," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, Part A, pp. 4821–4835, Sep. 2022.
I. Cetin, M. Stephens, O. Camara, and M. A. González Ballester, "Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders," Computerized Medical Imaging and Graphics, vol. 104, Mar. 2023, Art. no. 102158.
U. Hameed, M. Ur Rehman, A. Rehman, R. Damaševičius, A. Sattar, and T. Saba, "A deep learning approach for liver cancer detection in CT scans," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 7, Jan. 2024, Art. no. 2280558.
S. Al-Otaibi, M. Mujahid, A. R. Khan, H. Nobanee, J. Alyami, and T. Saba, "Dual Attention Convolutional AutoEncoder for Diagnosis of Alzheimer’s Disorder in Patients Using Neuroimaging and MRI Features," IEEE Access, vol. 12, pp. 58722–58739, 2024.
S. R. Waheed, N. M. Suaib, M. S. M. Rahim, A. R. Khan, S. A. Bahaj, and T. Saba, "Synergistic Integration of Transfer Learning and Deep Learning for Enhanced Object Detection in Digital Images," IEEE Access, vol. 12, pp. 13525–13536, 2024.
D. Alzu’bi et al., "Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans," Journal of Healthcare Engineering, vol. 2022, no. 1, 2022, Art. no. 3861161.
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Copyright (c) 2024 Mohamed Tounsi, Donya Y. Abdulhussain, Ahmad Taher Azar, Ahmed Al-Khayyat, Ibraheem Kasim Ibraheem
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