Uncertainty evaluation in a fuzzy classifier for microcalcifications in digital mammography

Ferrero, Alessandro; Salicone, Simona; Mencattini, Arianna; Rabottino, Giulia; Salmeri, Marcello

doi:10.1109/imtc.2010.5488124

Cited by 13 publications

(15 citation statements)

References 7 publications

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“…In the specific context, the sensitivity behavior also suggests the need for the implementation of a multivariate diagnostic system receiving in input not only the number of calcifications, but also the related statistics on the MC area distribution, shape of the clusters, average distance among calcifications in the clusters, to name a few [17]. In light of these considerations, further works will certainly investigate the case of MISO systems, accounting for effects like input variables inter-correlation and cross-sensitivity (e.g., selectivity).…”

Section: Sensitivity In Computerized Diagnosis Of Breast Calcificationsmentioning

confidence: 99%

A conceptual framework for concept definition in measurement: The case of ‘sensitivity’

Mencattini

Mari

2015

Measurement

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Section: Sensitivity In Computerized Diagnosis Of Breast Calcificationsmentioning

confidence: 99%

A conceptual framework for concept definition in measurement: The case of ‘sensitivity’

Mencattini

Mari

2015

Measurement

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“…• Rectal Temperature, the recent development of the FIS for breast cancer diagnosis performed by some of the authors [12], based on the idea of Andersson [13], an interesting and novel version of a FIS for medical investigation is proposed in this study. It is structured as a medical history (anamnesis)-semiotic-diagnosis process.…”

Section: Predictionmentioning

confidence: 99%

“…The so called Symptom-Outcome (SO) relationship able to describe the a priori significance of the symptom for the specific outcome. This step will be performed using the same procedure introduced in [12]. Further details about this procedure will be given in the following section.…”

Section: Predictionmentioning

confidence: 99%

“…In particular, standard methodologies exist to assess the significance of a predictor for a selected outcome. This step can be done for example evaluating the Incidence Level (IL), already computed in the FIS for the breast cancer diagnosis [12]. The Incidence Level is a numerical value in the range [0, 1], the greater as the more important is the considered symptom in the selected outcome.…”

Section: B Evaluate the Significance Of Each Predictor For The Selecmentioning

confidence: 99%

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A study on a novel scoring system for the evaluation of expected mortality in ICU-patients

Mencattini

Salmeri

Lordi

et al. 2011

2011 IEEE International Symposium on Medical Measurements and Applications

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Patients in the Intensive Care Unit (ICU) require huge resources because of the dysfunction of several of their vital organs. The heterogeneity and complexity of the ICU patients have generated interest in systems able to measure severity of illness as a method of predicting outcome, compare quality-of-care, analyze the costs of treating critically ill patients, evaluate stratification of clinical trials. The present work aims at developing a new scoring system to assess the severity of illness of patients admitted to general ICUs, by an observational, prospective, cohort study. The model will be developed in the period of study considering a population of the general ICU at the University Hospital Polyclinic of Tor Vergata so that the data obtained will be usable for Italian health care resource management and ICU performance evaluation within the hospital, the region, and nationwide. The proposed scoring system will revise the presently available systems, in the attempt to overcome their major drawbacks in the definition of the input variables, in the mathematical model employed to process them and the considered outcome. An important and qualifying point of this work lies in the attention paid to quantify and process the different sources of uncertainty, originated by the approximations introduced in the considered model and in the collection stage of the predictor variables

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“…Early detection of breast cancer is one of the most important factors affecting the prospect of recovery from the disease. For that cause, the mammography remains the most accurate and best tool in detecting breast cancer [2]. mammography is a private kind of x-ray imaging used to make detailed images of the breast.…”

Section: Introductionmentioning

confidence: 99%

Mammography Images Segmentation Based on Fuzzy Set and Thresholding

Salih

Kamil

2019

MJS

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Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy. This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis.

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Uncertainty evaluation in a fuzzy classifier for microcalcifications in digital mammography

Cited by 13 publications

References 7 publications

A conceptual framework for concept definition in measurement: The case of ‘sensitivity’

A conceptual framework for concept definition in measurement: The case of ‘sensitivity’

A study on a novel scoring system for the evaluation of expected mortality in ICU-patients

Mammography Images Segmentation Based on Fuzzy Set and Thresholding

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