Automated Diagnosis of Benign Prostatic Hyperplasia Using Deep Learning on RGB Prostate Images
Keywords:
Benign Prostatic Hyperplasia, Deep Learning, DenseNet121, Image Classification, Transfer Learning
Abstract
Benign Prostatic Hyperplasia (BPH) is a prevalent non-cancerous enlargement of the prostate gland in aging men, often requiring early diagnosis to prevent urinary complications and improve patient outcomes. Traditional diagnostic procedures are limited by subjectivity and accessibility, especially in under-resourced regions. This study proposes an automated diagnostic approach using a deep learning model based on DenseNet121 to classify RGB prostate images into BPH and normal categories. A region-specific dataset consisting of 176 labeled RGB images, collected from a clinical facility in Bangladesh, was used to train and evaluate the model. Pre-processing included image resizing, normalization, and data augmentation to enhance generalization. Transfer learning was employed to fine-tune the model, which was trained over 10 epochs using the Adam optimizer and cross-entropy loss. The model achieved a best validation accuracy of 94.12%, with a recall of 72.2% for BPH detection, demonstrating its ability to identify pathological patterns in simple imaging modalities. Despite challenges such as dataset size and imbalance, the findings indicate that RGB image-based deep learning models can support clinical diagnosis of BPH in low-resource settings. This work contributes a lightweight, accessible solution for prostate disease screening and provides a foundation for future research on scalable AI-assisted diagnostics.
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[2] A. Kaur and R. A. Reddy, “Empowering Diagnosis: The Role of CNNs in Prostate Disease Detection,” in 2024 International Conference on Innovation and Novelty in Engineering and Technology (INNOVA), Dec. 2024, pp. 1–6, doi: 10.1109/INNOVA63080.2024.10846946.
[3] X. Wang et al., “Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning,” Sci. Rep., vol. 7, no. 1, p. 15415, Nov. 2017, doi: 10.1038/s41598-017-15720-y.
[4] B. Hu et al., “Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images,” Cureus, Mar. 2021, doi: 10.7759/cureus.14108.
[5] A. H. M. Linkon, M. M. Labib, T. Hasan, M. Hossain, and M.-E.- Jannat, “Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study,” Informatics Med. Unlocked, vol. 24, p. 100582, 2021, doi: 10.1016/j.imu.2021.100582.
[6] F. M. Talaat, S. El-Sappagh, K. Alnowaiser, and E. Hassan, “Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture,” BMC Med. Inform. Decis. Mak., vol. 24, no. 1, p. 23, Jan. 2024, doi: 10.1186/s12911-024-02419-0.
[7] M. Zulhusni, “Implementation of DenseNet121 Architecture for Waste Type Classification,” Adv. Sustain. Sci. Eng. Technol., vol. 6, no. 3, 2024, doi: 10.26877/asset.v6i3.673.
[8] B. A. Dabwan, “Hand Gesture Classification for Individuals with Disabilities Using the DenseNet121 Model,” International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024. 2024, doi: 10.1109/APCI61480.2024.10616504.
[9] J. Potsangbam, “Classification of Breast Cancer Histopathological Images Using Transfer Learning with DenseNet121,” Procedia Computer Science, vol. 235. pp. 1990–1997, 2024, doi: 10.1016/j.procs.2024.04.188.
[10] O. Gonzalez-Ortiz, “Evaluating DenseNet121 Neural Network Performance for Cervical Pathology Classification,” Proceedings - IEEE Symposium on Computer-Based Medical Systems. pp. 297–302, 2024, doi: 10.1109/CBMS61543.2024.00056.
[11] M. Masab, M. U. Rehman, Z. Rafi, and W. T. Toor, “A Comparative Study of DenseNet121, VGG16, and Custom CNNs for Brain Tumor Classification using MRI Images,” in 2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), Nov. 2024, pp. 1–6, doi: 10.1109/ETECTE63967.2024.10824003.
[12] A. Raza, “Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection,” Int. J. Imaging Syst. Technol., vol. 34, no. 1, 2024, doi: 10.1002/ima.22957.
[13] D. Anitha, “Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier,” 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024. 2024, doi: 10.1109/NMITCON62075.2024.10698836.
[14] L. P. Kothala, “Multi-Class Classification of Intracranial Hemorrhages in a 3-Channel CT image by using a Transfer Learning based DenseNet121 model,” 2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022. 2022, doi: 10.1109/SMARTGENCON56628.2022.10084153.
[15] X. Zhou, “Environmental sound classification of western black-crowned gibbon habitat based on subspace method and DenseNet121,” IMCEC 2022 - IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference. pp. 880–884, 2022, doi: 10.1109/IMCEC55388.2022.10019838.
[16] D. Dharrao, “Efficient Classification of Rotten and Healthy Bell Peppers Using an Optimized DenseNet121 Model,” 2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024. 2024, doi: 10.1109/ICIICS63763.2024.10859674.
[17] T. Mubeen, “Analyzing the Classification Performance of DenseNet121 on Pre-processsed MIAS Dataset,” 2023 Global Conference on Wireless and Optical Technologies, GCWOT 2023. 2023, doi: 10.1109/GCWOT57803.2023.10064663.
[18] R. K. Pattanaik, “Breast Cancer Classification from Mammogram Images Using Extreme Learning Machine-Based DenseNet121 Model,” Genet. Res. (Camb)., vol. 2022, 2022, doi: 10.1155/2022/2731364.
[19] T. L. Do, “Classification of Surface Defects on Steel Sheet Images Using DenseNet121 Architecture,” Lecture Notes in Civil Engineering, vol. 442. pp. 731–737, 2024, doi: 10.1007/978-981-99-7434-4_74.
[20] R. K. Yadav, “A Hybrid Approach for Skin Cancer Detection and Classification using DenseNet121,” Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference, DELCON 2024. 2024, doi: 10.1109/DELCON64804.2024.10866536.
[21] S. P. C. Machina, S. S. Koduru, and S. Madichetty, “Solar Energy Forecasting Using Deep Learning Techniques,” in 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Jan. 2022, pp. 1–6, doi: 10.1109/PARC52418.2022.9726605.
[22] K. U. Devi, “Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN),” Journal of Ambient Intelligence and Humanized Computing. 2020, doi: 10.1007/s12652-020-02200-x.
[23] N. V. Y. Lankadasu, “Skin Cancer Classification Using a Convolutional Neural Network: An Exploration into Deep Learning,” 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024. pp. 1047–1052, 2024, doi: 10.1109/ICETSIS61505.2024.10459368.
[24] Y. Zhang, “An automatic cervical cell classification model based on improved DenseNet121,” Sci. Rep., vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-87953-1.
[25] V. Yogabalajee, “Soybean Leaf Disease Classification using Enhanced Densenet121,” 10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024. pp. 1515–1520, 2024, doi: 10.1109/ICACCS60874.2024.10717087.
Published
2025-05-30
Section
Articles
