CNN-based Classification of Bladder Tissue Lesions from Endoscopy Images

Lutviana Lutviana; Rian Ardianto; Purwono

doi:10.25299/itjrd.2025.17867

Authors

Lutviana Lutviana Departement of Informatics, Universitas Harapan Bangsa
Rian Ardianto Departement of Informatics, Universitas Harapan Bangsa
Purwono Departement of Informatics, Universitas Harapan Bangsa

DOI:

https://doi.org/10.25299/itjrd.2025.17867

Keywords:

Bladder cancer, lesion classification, Convolutional Neural Network, Endoscopic image

Abstract

Bladder cancer is one type of tumor that frequently occurs in the urinary system, and early diagnosis is essential to improve the prognosis and survival of patients. The study aims to develop a Convolutional Neural Network (CNN) model for bladder tissue lesion classification from endoscopic images. This study uses a dataset consisting of 1754 images, which are divided into four classes: High-Grade Cancer (HGC), Low-Grade Cancer (LGC), Non-Specific Tissue (NST), and Non-Tumorous Lesion (NTL). The proposed CNN model showed a validation accuracy of 96.29%, with high recall, precision, and F1-score in most classes. The results show that CNN-based automated methods can improve efficiency and accuracy in the early diagnosis of bladder cancer, reduce manual visual interpretation errors, and improve the quality of patient care. This study suggests increasing the training data, especially for the NTL class, and applying more complex model architecture to better results.

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References

[1] M. Muharrom, “Klasifikasi Diagnosa Peradangan Kandung Kemih Menggunakan Metode Algoritma Naïve Bayes,” Indonesian Journal of Business Intelligence (IJUBI), vol. 3, no. 2, p. 31, Jan. 2021, doi: 10.21927/ijubi.v3i2.1472.

[2] D. Sugeng Supriyadi, D. Wanadi, and H. Setyono, “Prosedur Terapi Radiasi Eksterna pada Pasien Kanker Kandung Kemih dengan Metastasis Articulatio Coxae,” JRI (Jurnal Radiografer Indonesia), vol. 4, no. 2, pp. 82–85, Nov. 2021, doi: 10.55451/jri.v4i2.93.

[3] J. Li, C. Cheng, and J. Zhang, “Autoimmune diseases and the risk of bladder cancer: A Mendelian randomization analysis,” J Autoimmun, vol. 146, p. 103231, Jun. 2024, doi: 10.1016/j.jaut.2024.103231.

[4] X. Dong et al., “Clinical practice guideline on bladder cancer (Part I),” UroPrecision, vol. 1, no. 1, pp. 20–30, Mar. 2023, doi: 10.1002/uro2.11.

[5] S. Kural, G. Jain, S. Agarwal, P. Das, and L. Kumar, “Urinary extracellular vesicles-encapsulated miRNA signatures: A new paradigm for urinary bladder cancer diagnosis and classification,” Urologic Oncology: Seminars and Original Investigations, Apr. 2024, doi: 10.1016/j.urolonc.2024.03.006.

[6] T. Nohara et al., “Variations in photodynamic diagnosis for bladder cancer due to the quality of endoscopic equipment,” Photodiagnosis Photodyn Ther, vol. 37, p. 102628, Mar. 2022, doi: 10.1016/j.pdpdt.2021.102628.

[7] I. Anwar et al., “Prevalence and survival in patients with bladder cancer: a study in high cancer incidence zone,” Asian Pacific Journal of Health Sciences, vol. 9, no. 4, pp. 243–247, Jun. 2022, doi: 10.21276/apjhs.2022.9.4.48.

[8] K. Vaidya, O. Vijayanand Potdar, P. Charuchandra Bhide, and A. Vikram Patkar, “Clinico-pathological Profile of Bladder Cancer Patients in A Tertiary Care Centre,” Int J Sci Res, pp. 41–44, Apr. 2023, doi: 10.36106/ijsr/0701547.

[9] L. Dzakiyyah Zulfa, D. Salim, and A. Tirza Melia Silalahi, “Potensi ncRNA dan lncRNA Dalam Diagnosis Kanker kandung Kemih Non Invasif,” Cerdika: Jurnal Ilmiah Indonesia, vol. 1, no. 4, pp. 384–391, Apr. 2021, doi: 10.59141/cerdika.v1i4.52.

[10] P. R. Puspawati, S. A. Kristina, and C. Wiedyaningsih, “Dampak Merokok Terhadap Kematian Dini Akibat Kanker di Indonesia: Estimasi Years of Life Lost (YLL),” Majalah Farmaseutik, vol. 16, no. 1, pp. 101–106, Sep. 2020, doi: https://doi.org/10.22146/farmaseutik.v16i1.49790.

[11] D. Waruwu and R. Rosnelly, “Deteksi Penyakit Kanker Kandung Kemih Berdasarkan Pengolahan Citra Digital,” Journal of Machine Learning and Data Analytics, vol. 2, no. 1, pp. 1–5, Feb. 2023, [Online]. Available: https://journal.fkpt.org/index.php/malda/article/view/479

[12] R. Tryaka, “Faktor Prognostik terhadap Pemulihan Pasien Kanker Kandung Kemih Paska Operasi Radikal Sistektomi,” Universitas Gadjah Mada, 2020.

[13] M. S. Devi, J. A. Pandian, D. Umanandhini, V. V, and V. G. P, “Endoscopic Bladder Tissue Classification Using Seventeen Layered Deep Convolutional Neural Network,” in 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), IEEE, Jan. 2024, pp. 1–6. doi: 10.1109/ASSIC60049.2024.10507996.

[14] M. Amaouche, O. Karrakchou, M. Ghogho, A. El Ghazzaly, M. Alami, and A. Ameur, “Redefining Cystoscopy With AI: Bladder Cancer Diagnosis Using an Efficient Hybrid CNN-Transformer Model,” in 2024 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2024, pp. 3030–3036. doi: 10.1109/ICIP51287.2024.10647282.

[15] M. A. K. Raiaan et al., “A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks,” Decision Analytics Journal, vol. 11, p. 100470, Jun. 2024, doi: 10.1016/j.dajour.2024.100470.

[16] R. Wetteland, K. Engan, T. Eftestøl, V. Kvikstad, and E. Janssen, “Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks,” in Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, SCITEPRESS - Science and Technology Publications, 2019, pp. 320–327. doi: 10.5220/0007253603200327.

[17] S. M. R. Hashemi, H. Hassanpour, E. Kozegar, and T. Tan, “Cystoscopic Image Classification by Unsupervised Feature Learning and Fusion of Classifiers,” IEEE Access, vol. 9, pp. 126610–126622, 2021, doi: 10.1109/ACCESS.2021.3098510.

[18] Alimin and S. Riyadi, “Sistem Pengembangan Deteksi Kanker Prostat Berbasis Image Processing dengan Metode Convolutional Neural Network,” Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika, vol. 14, no. 2, pp. 52–63, Dec. 2022, doi: https://doi.org/10.35891/explorit.v14i2.3535.

[19] F. A. A. Harahap, R. M. Sinaga, K. Arifin, and K. S. S, “Implementasi Algoritma Convolutional Neural Network Untuk Mendeteksi Penyakit Ginjal,” Jurnal Teknologi Informasi, Komputer dan Aplikasinya (JTIKA, vol. 4, no. 2, pp. 212–219, Sep. 2022, doi: https://doi.org/10.29303/jtika.v4i2.202.

[20] J. F. Lazo et al., “Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images,” IEEE Trans Biomed Eng, vol. 70, no. 10, pp. 2822–2833, Oct. 2023, doi: 10.1109/TBME.2023.3265679.

[21] Y. Belotti, D. S. Jokhun, V. L. M. Valerio, T. W. Chong, and C. T. Lim, “Deep convolutional neural network accurately classifies different types of bladder cancer cells based on their pH fingerprints and morphology,” AIP Adv, vol. 13, no. 5, May 2023, doi: 10.1063/5.0120216.

[22] J. F. Lazo et al., “Endoscopic Bladder Tissue Classification Dataset,” Zenodo.