*Result*: Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.
Title:
Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.
Authors:
Emegano DI; Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey. declanikechukwu.emegano@neu.edu.tr.; Department of Biomedical Engineering, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey. declanikechukwu.emegano@neu.edu.tr., Mustapha MT; Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey., Ozsahin DU; Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey.; Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE.; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE., Ozsahin I; Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey., Uzun B; Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey. berna.uzun@neu.edu.tr.; Department of Mathematics, Near East University, Nicosia/TRNC, 99138, Mersin 10, Turkey. berna.uzun@neu.edu.tr.
Source:
Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Feb; Vol. 39 (1), pp. 604-619. Date of Electronic Publication: 2025 May 20.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
MeSH Terms:
References:
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Talaat FM, El-Sappagh S, Alnowaiser K, Hassan E. Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture. BMC Med Inform Decis Mak 2024;24:23. https://doi.org/10.1186/s12911-024-02419-0 . (PMID: 10.1186/s12911-024-02419-03826799410809762)
Satturwar S, Parwani AV. Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications. Advances in Anatomic Pathology 2024;31:136–44. https://doi.org/10.1097/PAP.0000000000000425 . (PMID: 10.1097/PAP.000000000000042538179884)
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 2020;53:5455–516. https://doi.org/10.1007/s10462-020-09825-6 . (PMID: 10.1007/s10462-020-09825-6)
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Zhang L, Bian Y, Jiang P, Zhang F. A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects. Applied Sciences 2023;13:5260. https://doi.org/10.3390/app13095260 . (PMID: 10.3390/app13095260)
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Hao J, Kosaraju SC, Tsaku NZ, Song DH, Kang M. PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data. Biocomputing 2020, Kohala Coast, Hawaii, USA: WORLD SCIENTIFIC; 2019, p. 355–66. https://doi.org/10.1142/9789811215636_0032 .
Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods 2020;179:3–13. https://doi.org/10.1016/j.ymeth.2020.05.012 . (PMID: 10.1016/j.ymeth.2020.05.01232442672)
Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Medical Image Analysis 2021;67:101813. https://doi.org/10.1016/j.media.2020.101813 . (PMID: 10.1016/j.media.2020.10181333049577)
Tsuneki M, Abe M, Kanavati F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics 2022;12:768. https://doi.org/10.3390/diagnostics12030768 . (PMID: 10.3390/diagnostics12030768353283218947489)
Tsuneki M, Abe M, Ichihara S, Kanavati F. Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer. BMC Cancer 2023;23:11. https://doi.org/10.1186/s12885-022-10488-5 . (PMID: 10.1186/s12885-022-10488-5366002039814218)
Garraway IP, Carlsson SV, Nyame YA, Vassy JL, Chilov M, Fleming M, et al. Prostate Cancer Foundation Screening Guidelines for Black Men in the United States. NEJM Evidence 2024;3. https://doi.org/10.1056/EVIDoa2300289 .
Pinckaers H, Bulten W, Van Der Laak J, Litjens G. Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels. IEEE Trans Med Imaging 2021;40:1817–26. https://doi.org/10.1109/TMI.2021.3066295 . (PMID: 10.1109/TMI.2021.306629533729928)
Alheejawi S, Berendt R, Jha N, Maity SP, Mandal M. Detection of malignant melanoma in H&E-stained images using deep learning techniques. Tissue and Cell 2021;73:101659. https://doi.org/10.1016/j.tice.2021.101659 . (PMID: 10.1016/j.tice.2021.10165934634635)
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Han X, Jin R. A Small Sample Image Recognition Method Based on ResNet and Transfer Learning. 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), Beijing, China: IEEE; 2020, p. 76–81. https://doi.org/10.1109/ICCIA49625.2020.00022 .
Deep Learning Model Based on ResNet-50 for Beef Quality Classification. Inf Sci Lett 2023;12:289–97. https://doi.org/10.18576/isl/120124 .
Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Computers in Biology and Medicine 2022;144:105350. https://doi.org/10.1016/j.compbiomed.2022.105350 . (PMID: 10.1016/j.compbiomed.2022.105350353055018890789)
Oğuz A, Ertuğrul ÖF. Introduction to deep learning and diagnosis in medicine. Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods, Elsevier; 2023, p. 1–40. https://doi.org/10.1016/B978-0-323-96129-5.00003-2 .
Kang Z, Xiao E, Li Z, Wang L. Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions. Academic Radiology 2024;31:2412–23. https://doi.org/10.1016/j.acra.2023.12.042 . (PMID: 10.1016/j.acra.2023.12.04238302387)
Huang X, Li Z, Zhang M, Gao S. Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images. Front Oncol 2022;12:994950. https://doi.org/10.3389/fonc.2022.994950 . (PMID: 10.3389/fonc.2022.994950362373119552083)
Borawar L, Kaur R. ResNet: Solving Vanishing Gradient in Deep Networks. In: Mahapatra RP, Peddoju SK, Roy S, Parwekar P, editors. Proceedings of International Conference on Recent Trends in Computing, vol. 600, Singapore: Springer Nature Singapore; 2023, p. 235–47. https://doi.org/10.1007/978-981-19-8825-7_21 .
Jusman Y. Comparison of Prostate Cell Image Classification Using CNN: ResNet-101 and VGG-19. 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia: IEEE; 2023, p. 74–8. https://doi.org/10.1109/ICCSCE58721.2023.10237088 .
Hasan N, Bao Y, Shawon A, Huang Y. DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN COMPUT SCI 2021;2:389. https://doi.org/10.1007/s42979-021-00782-7 . (PMID: 10.1007/s42979-021-00782-7343374328300985)
Seyer Cagatan A, Taiwo Mustapha M, Bagkur C, Sanlidag T, Ozsahin DU. An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model. Diagnostics 2022;13:81. https://doi.org/10.3390/diagnostics13010081 .
Salama WM, Aly MH. Prostate cancer detection based on deep convolutional neural networks and support vector machines: a novel concern level analysis. Multimed Tools Appl 2021;80:24995–5007. https://doi.org/10.1007/s11042-021-10849-5 . (PMID: 10.1007/s11042-021-10849-5)
Huang H, You Z, Cai H, Xu J, Lin D. Fast detection method for prostate cancer cells based on an integrated ResNet50 and YoloV5 framework. Computer Methods and Programs in Biomedicine 2022;226:107184. https://doi.org/10.1016/j.cmpb.2022.107184 . (PMID: 10.1016/j.cmpb.2022.10718436288685)
Jusman Y, Nurkholid MAF, Utomo F. Prostate Image Classification Using Pretrained Models: GoogLeNet and ResNet-50. 2021 15th International Conference on Signal Processing and Communication Systems (ICSPCS), Sydney, Australia: IEEE; 2021, p. 1–6. https://doi.org/10.1109/ICSPCS53099.2021.9660334 .
Marletta S, Eccher A, Martelli FM, Santonicco N, Girolami I, Scarpa A, et al. Artificial intelligence–based algorithms for the diagnosis of prostate cancer: A systematic review. American Journal of Clinical Pathology 2024;161:526–34. https://doi.org/10.1093/ajcp/aqad182 . (PMID: 10.1093/ajcp/aqad18238381582)
Xu D, Xu Z. Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives. Artificial Intelligence in Medicine 2024;156:102950. https://doi.org/10.1016/j.artmed.2024.102950 . (PMID: 10.1016/j.artmed.2024.10295039163727)
Talaat FM, El-Sappagh S, Alnowaiser K, Hassan E. Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture. BMC Med Inform Decis Mak 2024;24:23. https://doi.org/10.1186/s12911-024-02419-0 . (PMID: 10.1186/s12911-024-02419-03826799410809762)
Satturwar S, Parwani AV. Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications. Advances in Anatomic Pathology 2024;31:136–44. https://doi.org/10.1097/PAP.0000000000000425 . (PMID: 10.1097/PAP.000000000000042538179884)
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 2020;53:5455–516. https://doi.org/10.1007/s10462-020-09825-6 . (PMID: 10.1007/s10462-020-09825-6)
Pan H, Pang Z, Wang Y, Wang Y, Chen L. A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects. IEEE Access 2020;8:119951–60. https://doi.org/10.1109/ACCESS.2020.3005450 . (PMID: 10.1109/ACCESS.2020.3005450)
Cui X, Zhou Y, Zhao C, Li J, Zheng X, Li X, et al. A Multiscale Hybrid Attention Networks Based on Multiview Images for the Diagnosis of Parkinson’s Disease. IEEE Trans Instrum Meas 2024;73:1–11. https://doi.org/10.1109/TIM.2023.3315407 . (PMID: 10.1109/TIM.2023.3315407)
Liang J, Cao J, Sun G, Zhang K, Van Gool L, Timofte R. SwinIR: Image Restoration Using Swin Transformer, 2021, p. 1833–44.
Zhang L, Bian Y, Jiang P, Zhang F. A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects. Applied Sciences 2023;13:5260. https://doi.org/10.3390/app13095260 . (PMID: 10.3390/app13095260)
Wen L, Li X, Gao L. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput & Applic 2020;32:6111–24. https://doi.org/10.1007/s00521-019-04097-w . (PMID: 10.1007/s00521-019-04097-w)
Hao J, Kosaraju SC, Tsaku NZ, Song DH, Kang M. PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data. Biocomputing 2020, Kohala Coast, Hawaii, USA: WORLD SCIENTIFIC; 2019, p. 355–66. https://doi.org/10.1142/9789811215636_0032 .
Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods 2020;179:3–13. https://doi.org/10.1016/j.ymeth.2020.05.012 . (PMID: 10.1016/j.ymeth.2020.05.01232442672)
Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Medical Image Analysis 2021;67:101813. https://doi.org/10.1016/j.media.2020.101813 . (PMID: 10.1016/j.media.2020.10181333049577)
Tsuneki M, Abe M, Kanavati F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics 2022;12:768. https://doi.org/10.3390/diagnostics12030768 . (PMID: 10.3390/diagnostics12030768353283218947489)
Tsuneki M, Abe M, Ichihara S, Kanavati F. Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer. BMC Cancer 2023;23:11. https://doi.org/10.1186/s12885-022-10488-5 . (PMID: 10.1186/s12885-022-10488-5366002039814218)
Garraway IP, Carlsson SV, Nyame YA, Vassy JL, Chilov M, Fleming M, et al. Prostate Cancer Foundation Screening Guidelines for Black Men in the United States. NEJM Evidence 2024;3. https://doi.org/10.1056/EVIDoa2300289 .
Pinckaers H, Bulten W, Van Der Laak J, Litjens G. Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels. IEEE Trans Med Imaging 2021;40:1817–26. https://doi.org/10.1109/TMI.2021.3066295 . (PMID: 10.1109/TMI.2021.306629533729928)
Alheejawi S, Berendt R, Jha N, Maity SP, Mandal M. Detection of malignant melanoma in H&E-stained images using deep learning techniques. Tissue and Cell 2021;73:101659. https://doi.org/10.1016/j.tice.2021.101659 . (PMID: 10.1016/j.tice.2021.10165934634635)
Ashour AS, Guo Y. Advanced optimization-based neutrosophic sets for medical image denoising. Neutrosophic Set in Medical Image Analysis, Elsevier; 2019, p. 101–21. https://doi.org/10.1016/B978-0-12-818148-5.00005-9 .
Han X, Jin R. A Small Sample Image Recognition Method Based on ResNet and Transfer Learning. 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), Beijing, China: IEEE; 2020, p. 76–81. https://doi.org/10.1109/ICCIA49625.2020.00022 .
Deep Learning Model Based on ResNet-50 for Beef Quality Classification. Inf Sci Lett 2023;12:289–97. https://doi.org/10.18576/isl/120124 .
Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Computers in Biology and Medicine 2022;144:105350. https://doi.org/10.1016/j.compbiomed.2022.105350 . (PMID: 10.1016/j.compbiomed.2022.105350353055018890789)
Oğuz A, Ertuğrul ÖF. Introduction to deep learning and diagnosis in medicine. Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods, Elsevier; 2023, p. 1–40. https://doi.org/10.1016/B978-0-323-96129-5.00003-2 .
Kang Z, Xiao E, Li Z, Wang L. Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions. Academic Radiology 2024;31:2412–23. https://doi.org/10.1016/j.acra.2023.12.042 . (PMID: 10.1016/j.acra.2023.12.04238302387)
Huang X, Li Z, Zhang M, Gao S. Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images. Front Oncol 2022;12:994950. https://doi.org/10.3389/fonc.2022.994950 . (PMID: 10.3389/fonc.2022.994950362373119552083)
Borawar L, Kaur R. ResNet: Solving Vanishing Gradient in Deep Networks. In: Mahapatra RP, Peddoju SK, Roy S, Parwekar P, editors. Proceedings of International Conference on Recent Trends in Computing, vol. 600, Singapore: Springer Nature Singapore; 2023, p. 235–47. https://doi.org/10.1007/978-981-19-8825-7_21 .
Jusman Y. Comparison of Prostate Cell Image Classification Using CNN: ResNet-101 and VGG-19. 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia: IEEE; 2023, p. 74–8. https://doi.org/10.1109/ICCSCE58721.2023.10237088 .
Hasan N, Bao Y, Shawon A, Huang Y. DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN COMPUT SCI 2021;2:389. https://doi.org/10.1007/s42979-021-00782-7 . (PMID: 10.1007/s42979-021-00782-7343374328300985)
Seyer Cagatan A, Taiwo Mustapha M, Bagkur C, Sanlidag T, Ozsahin DU. An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model. Diagnostics 2022;13:81. https://doi.org/10.3390/diagnostics13010081 .
Salama WM, Aly MH. Prostate cancer detection based on deep convolutional neural networks and support vector machines: a novel concern level analysis. Multimed Tools Appl 2021;80:24995–5007. https://doi.org/10.1007/s11042-021-10849-5 . (PMID: 10.1007/s11042-021-10849-5)
Huang H, You Z, Cai H, Xu J, Lin D. Fast detection method for prostate cancer cells based on an integrated ResNet50 and YoloV5 framework. Computer Methods and Programs in Biomedicine 2022;226:107184. https://doi.org/10.1016/j.cmpb.2022.107184 . (PMID: 10.1016/j.cmpb.2022.10718436288685)
Jusman Y, Nurkholid MAF, Utomo F. Prostate Image Classification Using Pretrained Models: GoogLeNet and ResNet-50. 2021 15th International Conference on Signal Processing and Communication Systems (ICSPCS), Sydney, Australia: IEEE; 2021, p. 1–6. https://doi.org/10.1109/ICSPCS53099.2021.9660334 .
Contributed Indexing:
Keywords: Benign; Biopsy; Histological; Malignant; Prostate cancer; ResNet50
Entry Date(s):
Date Created: 20250520 Date Completed: 20260219 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12921011
DOI:
10.1007/s10278-025-01543-1
PMID:
40394318
Database:
MEDLINE
*Further Information*
*Prostate cancer is the most prevalent solid tumor in males and one of the most common causes of male mortality. It is the most common type of cancer in men, a major global public health issue, and accounts for up to 7.3% of all male cancer diagnoses worldwide. To optimize patient outcomes and ensure therapeutic success, an accurate diagnosis must be made promptly. To achieve this, we focused on using ResNet50, a convolutional neural network (CNN) architecture, to analyze prostate histological images to classify prostate cancer. ResNet50, due to its efficiency in medical image classification, was used to classify the histological images as benign or malignant. In this study, a total of 1276 prostate biopsy images were used on the ResNet50 model. We employed evaluation metrics such as accuracy, precision, recall, and F1 score. The results showed that the ResNet50 model performed excellently with an overall accuracy of 0.98, 1.00 as precision, 0.98 as recall, and 0.97 as F1 score for benign. The malignant histological image has 0.99, 0.98, and 0.97 as precision, recall, and F1 scores. It also recorded a 95% confidence interval (CI) for accuracy as (0.91, 1.00) and a performance gain of 4.26% compared to MobileNet and CNN-RNN. The result of our model was also compared with the state-of-the-art (SOTA) DL models to ensure robustness. This study has demonstrated the potential of the ResNet50 model in the classification of prostate cancer. Again, the clinical integration of the results of this study will aid decision-makers in enhancing patient outcomes.
(© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)*
*Declarations. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Federal Medical Centre Lokoja, Nigeria (FMCL/HREC/Vol./2023/192). Competing interests: The authors declare no competing interests.*