Neuro-fuzzy system for prostate cancer diagnosis

Benecchi, Luigi

doi:10.1016/j.urology.2006.03.003

Cited by 58 publications

(30 citation statements)

References 32 publications

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“…[78] Recently, Luigi Benecchi[9] used the neuro-fuzzy system to diagnose prostate cancer and compared its predictive accuracy with that obtained by total PSA and percent-free PSA. He concluded that the predictive accuracy of the neuro-fuzzy was superior to that of the PSA and %f PSA.…”

Section: Discussionmentioning

confidence: 99%

Prediction of biochemical failure in localized carcinoma of prostate after radical prostatectomy by neuro-fuzzy

et al. 2007

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Objective:To predict biochemical failure in localized prostate cancer after radical prostatectomy using preoperative variables.Materials and Methods:Twenty-six patients of early carcinoma of prostate underwent open retropubic radical prostatectomy from June 2002 to June 2006. Preoperative variables included age, family history, digital rectal examination, serum prostatic specific antigen (S. PSA), prostate biopsy Gleason score, MRI of pelvis variables like periprostatic extension, seminal vesical invasion, weight of gland and pathological stage. With application of neuro-fuzzy, these variables were fed into system as input and output, that is S. PSA at six months (predicted value) was calculated. Neuro-fuzzy system is a system to combine fuzzy system with learning techniques derived from neural networks. Here, we applied Takagi Sugeno Kang model (TSK) due to its close solution to our aim. All the patients were followed up for a minimum of six months. At six month S. PSA of all patients was done (observed value). Predicted and observed values were compared.Result:Predicted and observed values were plotted on 1:1 slop line. Coefficient of correlation was 0.9935.Conclusion:Coefficient of correlation is close to one. It indicates that the neuro-fuzzy is accurate in predicting biochemical failure in localized carcinoma of prostate after radical prostatectomy.

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Section: Discussionmentioning

confidence: 99%

Prediction of biochemical failure in localized carcinoma of prostate after radical prostatectomy by neuro-fuzzy

et al. 2007

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“…Benecchi (2006) predicted the presence of prostate cancer using a co-active neuro-fuzzy inference system (CANFIS). CANFIS initially adopts a Genetic Algorithm (GA) to simultane- The proposed neuro-fuzzy system also performed better than the AJCC pTNM Staging Nomogram (Edge et al, 2010) which is commonly used by clinicians.…”

Section: Neuro-fuzzy Approachesmentioning

confidence: 99%

“…nomogram) that only achieved 455 and Area Under the Curve of 69.3%. It should be noted that in the study by Castanho et al (2013), the Genetic Algorithm was used for membership function tuning, whereas in previous studies such as that by Benecchi (2006), the Genetic Algorithm was used for feature extraction. This demonstrates the suitability of the Genetic Algorithm for tuning membership functions and feature 460 extraction tasks.…”

Section: Genetic-fuzzy Approachesmentioning

confidence: 99%

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Cosma

Brown

Archer

et al. 2017

Expert Systems with Applications

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Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known as ing the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.

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“…Benecchi [5] proposed a system for diagnosing Menigioma by integrating Fuzzy C-Mean with region growing techniques. The outcome of the system shows that the system effectively detects tumors in the images from the patient's images that contained Menigioma.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic Based Approach for Automation of Emotion Detection in Misophonia

Salankar¹,

Ahuja²,

Chaurasia³

et al. 2017

IJCSIT

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Human being mostly categories into two types, the one who follow distress cycle and other who believe on wellness cycle. Output of distress cycle is decreased productivity, decreased enjoyment and decreased intimacy, whereas output of wellness cycle is increased productivity, increased enjoyment and increased intimacy which is essential for life. Reason of distress could be anything like emotion, disease, environment, family, pain. One of the mostly unexplored areas of cause of distress is misophonia. It is a disorder related to various senses like sound, sight, smell, taste and touch. This disorder is not in DSM-V, underscoring that it is not known by psychiatric and medical communities. We have proposed an automatic fuzzy approach system for identification of intensity of emotions related to various triggers on the basis of age group.

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Neuro-fuzzy system for prostate cancer diagnosis

Cited by 58 publications

References 32 publications

Prediction of biochemical failure in localized carcinoma of prostate after radical prostatectomy by neuro-fuzzy

Prediction of biochemical failure in localized carcinoma of prostate after radical prostatectomy by neuro-fuzzy

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Fuzzy Logic Based Approach for Automation of Emotion Detection in Misophonia

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