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Shih, D.; Shih, P.; Wu, T.; Lee, C.; Shih, M. Deep Learning in Distinguishing Bladder Cancer from Cystitis. Encyclopedia. Available online: https://encyclopedia.pub/entry/51392 (accessed on 05 July 2024).
Shih D, Shih P, Wu T, Lee C, Shih M. Deep Learning in Distinguishing Bladder Cancer from Cystitis. Encyclopedia. Available at: https://encyclopedia.pub/entry/51392. Accessed July 05, 2024.
Shih, Dong-Her, Pai-Ling Shih, Ting-Wei Wu, Chen-Xuan Lee, Ming-Hung Shih. "Deep Learning in Distinguishing Bladder Cancer from Cystitis" Encyclopedia, https://encyclopedia.pub/entry/51392 (accessed July 05, 2024).
Shih, D., Shih, P., Wu, T., Lee, C., & Shih, M. (2023, November 10). Deep Learning in Distinguishing Bladder Cancer from Cystitis. In Encyclopedia. https://encyclopedia.pub/entry/51392
Shih, Dong-Her, et al. "Deep Learning in Distinguishing Bladder Cancer from Cystitis." Encyclopedia. Web. 10 November, 2023.
Deep Learning in Distinguishing Bladder Cancer from Cystitis
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This entry is adapted from the peer-reviewed paper 10.3390/math11194118

Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment.

Cystitis Bladder Cancer deep learning

1. Introduction

According to the latest global cancer data for 2020 released by the International Agency for Research on Cancer (IARC) of the World Health Organization, cancer caused nearly 10 million deaths in 2020 [1]. As one of the top ten most common cancers in the world, Bladder Cancer is the tumor with the highest cost associated with lifetime treatment and one of the tumors with the most significant impact on postoperative quality of life [2]. Even in 2019, an estimated 17,600 people died from Bladder Cancer in the United States, a figure that accounts for 2.9% of all cancer deaths [3].
Bladder Cancer is one of the most common and expensive human malignancies to treat [4][5][6]; Bladder Cancer refers to various malignant tumors in the bladder. Possible symptoms include frequent urination, dysuria, pain in urination, low back pain, abdominal pain, and severe painless hematuria [7]. In the general adult population, invisible hematuria is found in 2–7% of men and 3–15% of women [8]; therefore, it cannot be made cost-effective for screening [9]. Bladder Cancer may initially be misdiagnosed as Cystitis or infection, which may be a complication during the early growth of the tumor before clinical diagnosis [10]. Previous studies have also suggested that Bladder Cancer may be mistaken for Interstitial Cystitis [11][12]. Because mass or thickening of the bladder wall is the dominant imaging feature of Eosinophilic Cystitis, similar to Bladder Cancer, differentiation from Bladder Cancer can be challenging [13]. In addition, Interstitial Cystitis (IC) may be misdiagnosed as Cystitis, leading to the misdiagnosis of Bladder Cancer [14]. On imaging, it sometimes presents masses that mimic various papillary urethral tumors and are misdiagnosed as Bladder Cancer before surgery [15].
Clinical chemistry tests and urine analysis are the main diagnostic screening tests in clinical laboratories [16], and each change in the test can be interpreted as a relationship with the disease, such as complete urine analysis including color, clarity, specific gravity, and chemical analysis according to the past literature. Furthermore, urine sediment examination [17] and chemical analysis are usually performed using a strip system to check for pH, glucose, ketones, occult blood, bilirubin, and protein [18]; however, some diseases may present abnormal results of chemical analysis, for example, ketonuria can be found in the urine of diabetic patients. At the same time, hematuria indicates bleeding in the urinary tract [19]. In addition, the examination of urinary sediment may reveal crystals, red blood cells, white blood cells, bacteria, and tubules, which provide information about the urinary tract system [20]; thus, interpreting clinical tests alone can lead to misleading diagnoses [21]. Laboratory test results can be interpreted by experienced clinicians but can also be integrated and interpreted in combination with artificial intelligence (AI), such as machine learning algorithms [22].
Machine learning (ML) is a type of artificial intelligence that enables it to learn independently from data without human intervention [23]. In addition to various machine learning algorithms often used in medical research, examples such as decision trees, random forests, XGBoost, and GBM have been frequently used in medical research [24][25][26]. Recently, many applied machine learning studies have been introduced into clinical practice, and machine learning has become a powerful tool to improve the accuracy of cancer diagnosis and prognosis. For example, Garapati et al. [27] established an objective computer-aided system to identify the stage of Bladder Cancer through CT urography. In addition, machine learning is also applied to metabolomics to identify early and late stages of Bladder Cancer [28], and Tsai et al. [29] applied machine learning to predict Bladder Cancer based on clinical laboratory data.

2. Bladder Cancer and Cystitis

2.1. Cystitis

Cystitis is a generic term used to define any bladder inflammation, which can be acute or chronic. At the same time, the severity can range from mild discomfort in the lower abdomen to life-threatening bleeding [30]. There are several categories to describe the various causes of Cystitis, divided into infectious, radiation, chemical, mechanical, interstitial Cystitis/chronic pelvic pain syndrome, and several conditions disguised as Cystitis. However, on a broader level, Cystitis can be divided into infectious and noninfectious. Patients with infectious Cystitis often complain of irritating emptying symptoms, difficulty urinating, frequency, urgency, and pain in the pubic hair, with severe bleeding occurring only in rare cases. Another major category of Cystitis is sterile or noninfectious Cystitis, which can be caused by radiation and chemical irritations. Unlike infection-induced Cystitis, noninfectious Cystitis is more clinically severe and can cause extreme pain, hematuria, and irritating emptying symptoms [30].
Interstitial Cystitis (IC) is a unique chronic syndrome in that it does not fit the classical distinction of infectious or noninfectious Cystitis [30]. Characterized by a range of lower urinary tract irritation symptoms and pain, the broad clinical definition of IC includes any patient who complains of urgency, frequency, and pelvic/perineal pain in the absence of bacterial infection or cancer [31]. Common symptoms include frequent urination, nocturnal urination, the urgency to urinate, bladder allergy, bladder discomfort, and bladder pain [32]. Unfortunately, IC is difficult to diagnose. To do this, clinicians must rule out urinary or vaginal infections, Bladder Cancer, bladder inflammation or infections caused by radiation therapy, eosinophilic and tuberculous Cystitis, kidney stones, endometriosis, neurological disorders, sexually transmitted diseases, low-count bacteria in the urine, and male prostatitis. Finally, Cystoscopy performed under general or regional anesthesia is used [33].
Eosinophilic Cystitis is a rare inflammatory disease of the bladder with uncertain etiology, first described by Brown [34] as eosinophilic granuloma on the bladder wall. Although many cases have been reported in adult and pediatric populations, their etiology remains elusive [35]. The most common symptoms are frequency, dysuria, urgency, pain, and hematuria, and the typical clinical findings are bladder mass, peripheral eosinophilic thickening, and bladder wall thickening [36].

2.2. Bladder Cancer

Bladder Cancer is the second most common malignancy of the urinary tract, accounting for approximately 3.2% of all cancers worldwide, and it is a significant cause of cancer morbidity and mortality. Most Bladder Cancers are diagnosed after the presence of macrohematuria [37], and the majority of cases (80%) occur in people over 65 years of age, with the highest incidence in people aged 85–89 years [38]. The most common symptom of Bladder Cancer is severe painless hematuria. In addition, unexplained symptoms of frequent urination, urgency, or irritating emptying should alert clinicians to the possibility of Bladder Cancer [7]. Bladder Cancer is invasive and non-invasive, with non-invasive having a reasonable risk and prognosis but muscle-aggressive having a poor prognosis [39].
The clinical spectrum of Bladder Cancer can be divided into three categories: Prognosis, management, and treatment goals. The first category includes non-muscular aggressive tumors, where treatment is designed to reduce recurrence and prevent progression to more advanced stages. The second category includes muscle-invasive lesions, where the goal of treatment is to determine whether the bladder should be removed or preserved without affecting survival and determine whether the primary lesion can be managed independently or whether the patient is at high risk of long-distance transmission, which requires a systematic approach to improve the likelihood of a cure. Finally, the critical issue is prolonging the quantity and quality of life, including metastatic diseases. Many agents with different mechanisms of action have antitumor effects against this disease [40].
Bladder Cancer does have the same symptoms as other diseases in its early stages, such as kidney cancer, prostate cancer, interstitial Cystitis, kidney stones, benign prostatic hyperplasia, and trauma [41]. Bladder Cancer may initially be misdiagnosed as Cystitis or infection, which may be a complication during early tumor growth before clinical diagnosis [42]. The symptoms of in situ Bladder Cancer and chronic Cystitis are similar, such as hematuria, frequent urination, and lower abdominal discomfort, and some patients even have urinary incontinence. If a patient has been treated for chronic Cystitis with antibiotics, cancer cells will take the opportunity to spread. While bladder inflammatory diseases such as interstitial Cystitis are sometimes challenging, they are often misdiagnosed [43].

3. Deep Learning in Distinguishing Bladder Cancer from Cystitis 

Bladder Cancer is the ninth most common malignancy worldwide [5], and clinicians use knowledge from different specialties to analyze histological, clinical, and demographic information [44]. Statistical methods such as Cox regression, logistic regression, and Kaplan–Meier estimators are commonly used in the analysis. For example, Kaplan–Meier methods and Cox proportional risk models were used to assess prognostic factors for recurrence, progression, and disease mortality in patients with Bladder Cancer [45]. Logistic regression based on 12 variables was used to determine predictors of 5-year overall survival in patients with Bladder Cancer who underwent radical cystectomy (Bassi et al., 2007). However, with the rapid development of health technology and informatics, the accuracy of predictions largely depends on the efficient integration of information from data obtained from various sources (clinical or pathological), which makes traditional statistical analysis relying on clinician knowledge and experience a difficult task. For example, regression modeling is a standard statistical technique that often requires some explicit assumptions about the relationship between the data that may not be valid [46]. Therefore, machine learning has been introduced into medicine to overcome the problems of statistical methods and reveal the knowledge hidden in complex clinical data [47].
In medicine and healthcare, machine learning has been applied to personalized and predictive medicine [48], cancer diagnosis and detection [49], and prevention and treatment policy research [44]. For Bladder Cancer, reliable predictions of patients undergoing cystectomy were achieved using an artificial neural network (ANN) prediction model and optimized by genetic algorithms (GA) [50]. This system has the potential for widespread use in medical decision support. In addition, Ji et al. [51] used ANN and radial basis function networks to predict the survival rate of patients with Bladder Cancer. In addition, clinic pathological and molecular markers were also used to create an ANN model to predict the one-year survival rate of patients with invasive Bladder Cancer [52]. Relevant studies on patients with Bladder Cancer and Cystitis are summarized in Table 1.
Table 1. Machine learning in the medical field and studies on Cystitis or Bladder Cancer.
Studies on Cystitis or Bladder Cancer Method ACC Author
Prediction of one-year survival in patients with muscular invasive Bladder Cancer ANN 82% [52]
Prediction of survival in patients with Bladder Cancer after diagnosis RBF 85% [51]
Accuracy of prognosis in patients with radical cystectomy ANN, LR LR:75.9%
ANN:76.4%		 [46]
Distinguish between Cystitis and Bladder Cancer DT, RF, SVM, XGBoost GBM ACC:87.6% [29]
Test for Bladder Cancer Trasfer learning (CNN) ACC:96.9% [53]
Related studies in the medical field Subject Method Author
Medical image recognition Quantify tumor characteristics RF [23]
Complex data classification Heart disease and hypertension data classification Tree-based [24]
Hypertensive patient prediction RF
SVM
XGBoost		 [25]

References

  1. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer 2021, 149, 778–789.
  2. Panebianco, V.; Pecoraro, M.; Del Giudice, F.; Takeuchi, M.; Muglia, V.F.; Messina, E.; Cipollari, S.; Giannarini, G.; Catalano, C.; Narumi, Y. VI-RADS for Bladder Cancer: Current applications and future developments. J. Magn. Reson. Imaging 2022, 55, 23–36.
  3. Saginala, K.; Barsouk, A.; Aluru, J.S.; Rawla, P.; Padala, S.A.; Barsouk, A. Epidemiology of Bladder Cancer. Med. Sci. 2020, 8, 15.
  4. Svatek, R.S.; Hollenbeck, B.K.; Holmäng, S.; Lee, R.; Kim, S.P.; Stenzl, A.; Lotan, Y. The economics of Bladder Cancer: Costs and considerations of caring for this disease. Eur. Urol. 2014, 66, 253–262.
  5. Antoni, S.; Ferlay, J.; Soerjomataram, I.; Znaor, A.; Jemal, A.; Bray, F. Bladder Cancer incidence and mortality: A global overview and recent trends. Eur. Urol. 2017, 71, 96–108.
  6. Leow, J.J.; Cole, A.P.; Seisen, T.; Bellmunt, J.; Mossanen, M.; Menon, M.; Preston, M.A.; Choueiri, T.K.; Kibel, A.S.; Chung, B.I. Variations in the costs of radical cystectomy for Bladder Cancer in the USA. Eur. Urol. 2018, 73, 374–382.
  7. Kaufman, D.S.; Shipley, W.U.; Feldman, A.S. Bladder Cancer. Lancet 2009, 374, 239–249.
  8. Kamat, A.M.; Hahn, N.M.; Efstathiou, J.A.; Lerner, S.P.; Malmström, P.-U.; Choi, W.; Guo, C.C.; Lotan, Y.; Kassouf, W. Bladder Cancer. Lancet 2016, 388, 2796–2810.
  9. Carel, R.; Silverberg, D.; Kaminsky, R.; Aviram, A. Routine urinalysis (dipstick) findings in mass screening of healthy adults. Clin. Chem. 1987, 33, 2106–2108.
  10. Jhamb, M.; Lin, J.; Ballow, R.; Kamat, A.M.; Grossman, H.B.; Wu, X. Urinary tract diseases and Bladder Cancer risk: A case–control study. Cancer Causes Control. 2007, 18, 839–845.
  11. Duldulao, K.E.; Diokno, A.C.; Mitchell, B. Value of urinary cytology in women presenting with urge incontinence and/or irritative voiding symptoms. J. Urol. 1997, 157, 113–116.
  12. Tissot, W.D.; Diokno, A.C.; Peters, K.M. A referral center’s experience with transitional cell carcinoma misdiagnosed as interstitial Cystitis. J. Urol. 2004, 172, 478–480.
  13. Wong-You–Cheong, J.J.; Woodward, P.J.; Manning, M.A.; Davis, C.J. Inflammatory and nonneoplastic bladder masses: Radiologic-pathologic correlation. Radiographics 2006, 26, 1847–1868.
  14. Wu, E.Q.; Birnbaum, H.; Mareva, M.; Parece, A.; Huang, Z.; Mallett, D.; Taitel, H. Interstitial Cystitis: Cost, treatment and co-morbidities in an employed population. Pharmacoeconomics 2006, 24, 55–65.
  15. Kiliç, S.; Erguvan, R.; İpek, D.; Gökçe, H.; Güneş, A.; Aydin, N.; Baydinç, C. Polypoid Cystitis unrelated to indwelling catheters. Int. Urol. Nephrol. 2002, 34, 293–297.
  16. Hindmarsh, J.T.; Lyon, A.W. Strategies to promote rational clinical chemistry test utilization. Clin. Biochem. 1996, 29, 291–299.
  17. Echeverry, G.; Hortin, G.L.; Rai, A.J. Introduction to Urinalysis: Historical Perspectives and Clinical Application. In The Urinary Proteome; Humana Press: Totowa, NJ, USA, 2010; pp. 1–12.
  18. Simerville, J.A.; Maxted, W.C.; Pahira, J.J. Urinalysis: A comprehensive review. Am. Fam. Physician 2005, 71, 1153–1162.
  19. Lillian, A.; Mundt, K. Chemical analysis of urine. In Graff’s Textbook of Routine Urinalysis and Body Fluids; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2010; Volume 1, pp. 35–53.
  20. Cavanaugh, C.; Perazella, M.A. Urine sediment examination in the diagnosis and management of kidney disease: Core curriculum 2019. Am. J. Kidney Dis. 2019, 73, 258–272.
  21. Ismail, A.A. When laboratory tests can mislead even when they appear plausible. Clin. Med. 2017, 17, 329.
  22. Haymond, S.; McCudden, C. Rise of the machines: Artificial intelligence and the clinical laboratory. J. Appl. Lab. Med. 2021, 6, 1640–1654.
  23. Parmar, C.; Grossmann, P.; Bussink, J.; Lambin, P.; Aerts, H.J. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 2015, 5, 13087.
  24. Banerjee, M.; Reynolds, E.; Andersson, H.B.; Nallamothu, B.K. Tree-based analysis: A practical approach to create clinical decision-making tools. Circ. Cardiovasc. Qual. Outcomes 2019, 12, e004879.
  25. Chang, W.; Liu, Y.; Xiao, Y.; Yuan, X.; Xu, X.; Zhang, S.; Zhou, S. A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics 2019, 9, 178.
  26. Zhang, J.; Mucs, D.; Norinder, U.; Svensson, F. LightGBM: An effective and scalable algorithm for prediction of chemical toxicity–application to the Tox21 and mutagenicity data sets. J. Chem. Inf. Model. 2019, 59, 4150–4158.
  27. Garapati, S.S.; Hadjiiski, L.; Cha, K.H.; Chan, H.P.; Caoili, E.M.; Cohan, R.H.; Weizer, A.; Alva, A.; Paramagul, C.; Wei, J. Urinary Bladder Cancer staging in CT urography using machine learning. Med. Phys. 2017, 44, 5814–5823.
  28. Kouznetsova, V.L.; Kim, E.; Romm, E.L.; Zhu, A.; Tsigelny, I.F. Recognition of early and late stages of Bladder Cancer using metabolites and machine learning. Metabolomics 2019, 15, 94.
  29. Tsai, I.-J.; Shen, W.-C.; Lee, C.-L.; Wang, H.-D.; Lin, C.-Y. Machine learning in prediction of Bladder Cancer on clinical laboratory data. Diagnostics 2022, 12, 203.
  30. Haldar, S.; Dru, C.; Bhowmick, N.A. Mechanisms of hemorrhagic Cystitis. Am. J. Clin. Exp. Urol. 2014, 2, 199.
  31. Rovner, E.; Propert, K.J.; Brensinger, C.; Wein, A.J.; Foy, M.; Kirkemo, A.; Landis, J.R.; Kusek, J.W.; Nyberg, L.M.; Interstitial Cystitis Data Base Study Group. Treatments used in women with interstitial Cystitis: The interstitial Cystitis data base (ICDB) study experience. Urology 2000, 56, 940–945.
  32. Homma, Y.; Ueda, T.; Tomoe, H.; Lin, A.T.; Kuo, H.C.; Lee, M.H.; Lee, J.G.; Kim, D.Y.; Lee, K.S.; Committee IC, GInterstitial Cystitis Guideline Committee. Clinical guidelines for interstitial Cystitis and hypersensitive bladder syndrome. Int. J. Urol. 2009, 16, 597–615.
  33. Chancellor, M.B.; Yoshimura, N. Treatment of interstitial Cystitis. Urology 2004, 63, 85–92.
  34. Brown, E.W. Eosinophilic granuloma of the bladder. J. Urol. 1960, 83, 665–668.
  35. Sparks, S.; Kaplan, A.; DeCambre, M.; Kaplan, G.; Holmes, N. Eosinophilic Cystitis in the pediatric population: A case series and review of the literature. J. Pediatr. Urol. 2013, 9, 738–744.
  36. Mosholt KS, S.; Dahl, C.; Azawi, N.H. Eosinophilic Cystitis: Three cases, and a review over 10 years. Case Rep. 2014, 2014, bcr2014205708.
  37. Sanli, O.; Dobruch, J.; Knowles, M.A.; Burger, M.; Alemozaffar, M.; Nielsen, M.E.; Lotan, Y. Bladder Cancer. Nat. Rev. Dis. Prim. 2017, 3, 17022.
  38. Stroman, L.; Issa, R. Bladder Cancer. Surgery 2022, 40, 674–682.
  39. Hashemi, M.; Arani, H.Z.; Orouei, S.; Rostamnejad, E.; Ghorbani, A.; Khaledabadi, M.; Kakavand, A.; Tavakolpournegari, A.; Saebfar, H.; Heidari, H. Crosstalk of miRNAs with signaling networks in Bladder Cancer progression: Therapeutic, diagnostic and prognostic functions. Pharmacol. Res. 2022, 185, 106475.
  40. Clark, P.E.; Agarwal, N.; Biagioli, M.C.; Eisenberger, M.A.; Greenberg, R.E.; Herr, H.W.; Inman, B.A.; Kuban, D.A.; Kuzel, T.M.; Lele, S.M. Bladder Cancer. J. Natl. Compr. Cancer Netw. 2013, 11, 446–475.
  41. Sharp, V.J.; Barnes, K.T.; Erickson, B.A. Assessment of asymptomatic microscopic hematuria in adults. Am. Fam. Physician 2013, 88, 747–754.
  42. Kantor, A.F.; Hartge, P.; Hoover, R.N.; Narayana, A.S.; Sullivan, J.; Fraumeni, J.R.J. Urinary tract infection and risk of Bladder Cancer. Am. J. Epidemiol. 1984, 119, 510–515.
  43. Thorsteinsson, K.; Jensen, J.B. Misinterpretation resulting in a diagnosis of Bladder Cancer–A case emphasising the value of diagnostic reconsideration. Urol. Case Rep. 2022, 40, 101928.
  44. Cruz, J.A.; Wishart, D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006, 2, 117693510600200030.
  45. Millan-Rodriguez, F.; Chechile-Toniolo, G.; Salvador-Bayarri, J.; Palou, J.; Vicente-Rodriguez, J. Multivariate analysis of the prognostic factors of primary superficial Bladder Cancer. J. Urol. 2000, 163, 73–78.
  46. Bassi, P.; Sacco, E.; De Marco, V.; Aragona, M.; Volpe, A. Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for Bladder Cancer: A comparison with logistic regression analysis. BJU Int. 2007, 99, 1007–1012.
  47. Wang, G.; Lam, K.-M.; Deng, Z.; Choi, K.-S. Prediction of mortality after radical cystectomy for Bladder Cancer by machine learning techniques. Comput. Biol. Med. 2015, 63, 124–132.
  48. Weston, A.D.; Hood, L. Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine. J. Proteome Res. 2004, 3, 179–196.
  49. Mccarthy, J.F.; Marx, K.A.; Hoffman, P.E.; Gee, A.G.; O’neil, P.; Ujwal, M.L.; Hotchkiss, J. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann. N. Y. Acad. Sci. 2004, 1020, 239–262.
  50. Vukicevic, A.M.; Jovicic, G.R.; Stojadinovic, M.M.; Prelevic, R.I.; Filipovic, N.D. Evolutionary assembled neural networks for making medical decisions with minimal regret: Application for predicting advanced Bladder Cancer outcome. Expert Syst. Appl. 2014, 41, 8092–8100.
  51. Ji, W.; Naguib RN, G.; Ghoneim, M.A. Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis-associated Bladder Cancer. IEEE Trans. Inf. Technol. Biomed. 2003, 7, 218–224.
  52. Qureshi, K.N.; Naguib, R.N.; Hamdy, F.C.; Neal, D.E.; Mellon, J. Neural network analysis of clinicopathological and molecular markers in Bladder Cancer. J. Urol. 2000, 163, 630–633.
  53. Freitas, N.R.; Vieira, P.M.; Cordeiro, A.; Tinoco, C.; Morais, N.; Torres, J.; Anacleto, S.; Laguna, M.P.; Lima, E.; Lima, C.S. Detection of Bladder Cancer with feature fusion, transfer learning and CapsNets. Artif. Intell. Med. 2022, 126, 102275.
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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Dong-Her Shih
,
Pai-Ling Shih
,
Ting-Wei Wu
,
Chen-Xuan Lee
,
Ming-Hung Shih
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Update Date: 13 Nov 2023
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