Abstract

Technological innovations have produced remarkable results in the health care sector. In particular, computer-aided detection (CAD) systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, two different CAD systems able to detect and to localize microcalcification clusters in mammographic images are implemented. The two methods utilize an artificial neural network and a support vector machine, respectively, as classifier. Adopting the MIAS database as procedure testing, the performance of the two implemented systems are compared in terms of sensitivity, specificity, accuracy, free-response operating characteristic curves, and Cohen's kappa coefficient. The obtained values for the previous parameters show the efficiency of both methods to operate as second opinion in microcalcification cluster detection, improving the screening process efficiency.

Autore Pugliese

• Guaragnella Cataldo , Rizzi Maria

Tutti gli autori

• RIZZI M , D'ALOIA M , GUARAGNELLA C , CASTAGNOLO B

Titolo volume/Rivista

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS

Anno di pubblicazione

2012

ISSN

1083-4427

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

7

Ultimo Aggiornamento Citazioni

2017-04-22 03:20:59

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile