Literature DB >> 1450670

Results obtained by using a computerized image analysis system designed as an aid to diagnosis of cutaneous melanoma.

N Cascinelli1, M Ferrario, R Bufalino, S Zurrida, V Galimberti, L Mascheroni, C Bartoli, C Clemente.   

Abstract

Results obtained using a computerized image analysis system as an aid to clinical diagnosis of melanoma are reported. The system comprises a colour television camera connected through a digitizing board to a 386 personal computer. By means of original algorithms able to measure the shape, the colours and texture of a pigmented lesion of the skin, the system provides eight on/off indicators that are matched with the histological diagnosis to identify benign and malignant pigmented lesions. The chances that a given lesion is malignant increase with the increasing number of positive indicators. The training field of the system was constituted of images and data of 169 cutaneous lesions in 165 patients. Taking two positive indicators as the threshold between pigmented benign and malignant lesions, the efficiency of the system is 0.98, the positive predictive value is 0.45 and the negative predictive value is 0.95. These values were confirmed in a series of 44 pigmented lesions, 10 of which were melanoma, that constitute the present test series. The authors conclude that this computerized image analysis system should be regarded as a useful aid to diagnosis for a non-expert clinician. The system limit is transformation within a naevus.

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Year:  1992        PMID: 1450670     DOI: 10.1097/00008390-199209000-00004

Source DB:  PubMed          Journal:  Melanoma Res        ISSN: 0960-8931            Impact factor:   3.599


  5 in total

Review 1.  Melanoma Early Detection: Big Data, Bigger Picture.

Authors:  Tracy Petrie; Ravikant Samatham; Alexander M Witkowski; Andre Esteva; Sancy A Leachman
Journal:  J Invest Dermatol       Date:  2018-10-25       Impact factor: 8.551

2.  The role of spectrophotometry in the diagnosis of melanoma.

Authors:  Paolo A Ascierto; Marco Palla; Fabrizio Ayala; Ileana De Michele; Corrado Caracò; Antonio Daponte; Ester Simeone; Stefano Mori; Maurizio Del Giudice; Rocco A Satriano; Antonio Vozza; Giuseppe Palmieri; Nicola Mozzillo
Journal:  BMC Dermatol       Date:  2010-08-13

3.  Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults.

Authors:  Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams
Journal:  Cochrane Database Syst Rev       Date:  2018-12-04

Review 4.  Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms.

Authors:  Ammara Masood; Adel Ali Al-Jumaily
Journal:  Int J Biomed Imaging       Date:  2013-12-23

Review 5.  The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers.

Authors:  Yasuhiro Fujisawa; Sae Inoue; Yoshiyuki Nakamura
Journal:  Front Med (Lausanne)       Date:  2019-08-27
  5 in total

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