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| Title: | Performance of AI methods in detecting melanoma | |
| Author (s): | Kjoelen, A. Thompson, M.J. Umbaugh, S.E. Moss, Randy Hays Stoecker, W.V. | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Image Processing Laboratory | |
| Keywords: | 35 mm 35 mm color slides AI methods performance AIM numeric modeling tool artificial intelligence atypical moles benign tumors color skin tumor images computer vision computer vision methods feature extraction lst-Class software mean success rates medical diagnostic imaging medical image processing melanoma detection monotonically increasing success ratios skin training set size | |
| Issue Date: | 1995 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Kjoelen, A.; Thompson, M.J.; Umbaugh, S.E.; Moss, R.H.; Stoecker, W.V., "Performance of AI methods in detecting melanoma," Engineering in Medicine and Biology Magazine, IEEE , vol.14, no.4 pp.411-416, JulAug 1995 | |
| Abstract: | This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This was the case with both AIM and lst-Class, and indicates a high potential for success if a method can be found for discriminating between atypical moles and melanoma | |
| Type: | Article - Journal text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Performance of AI methods in detecting melanoma | |
| contributor.author | Kjoelen, A. | |
| contributor.author | Thompson, M.J. | |
| contributor.author | Umbaugh, S.E. | |
| contributor.author | Moss, Randy Hays | |
| contributor.author | Stoecker, W.V. | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Image Processing Laboratory | |
| subject | 35 mm | |
| subject | 35 mm color slides | |
| subject | AI methods performance | |
| subject | AIM numeric modeling tool | |
| subject | artificial intelligence | |
| subject | atypical moles | |
| subject | benign tumors | |
| subject | color skin tumor images | |
| subject | computer vision | |
| subject | computer vision methods | |
| subject | feature extraction | |
| subject | lst-Class software | |
| subject | mean success rates | |
| subject | medical diagnostic imaging | |
| subject | medical image processing | |
| subject | melanoma detection | |
| subject | monotonically increasing success ratios | |
| subject | skin | |
| subject | training set size | |
| date.issued | 1995 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Kjoelen, A.; Thompson, M.J.; Umbaugh, S.E.; Moss, R.H.; Stoecker, W.V., "Performance of AI methods in detecting melanoma," Engineering in Medicine and Biology Magazine, IEEE , vol.14, no.4 pp.411-416, JulAug 1995 | |
| identifier.issn | 0739-5175 | |
| identifier.pub.URI | ||
| description.abstract | This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This was the case with both AIM and lst-Class, and indicates a high potential for success if a method can be found for discriminating between atypical moles and melanoma | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T13:59:07Z | |
| date.available | 2007-04-05T13:59:07Z | |
| identifier.persist.URI | ||
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