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| Title: | Neural network diagnosis of malignant melanoma from color images | |
| Author (s): | Ercal, Fikret Chawla, A. Stoecker, W.V. Hsi-Chieh Lee Moss, Randy Hays | |
| Department/Lab Affiliations: | Computer Science Electrical and Computer Engineering Image Processing Laboratory | |
| Keywords: | 5 y United States artificial neural network classification automated cancer detection benign tumors color images deadliest skin cancer form discriminant features malignant melanoma medical diagnostic images medical image processing neural network diagnosis patient diagnosis relative tumor color skin skin tumor images tumor shape | |
| Issue Date: | 1994 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Ercal, F.; Chawla, A.; Stoecker, W.V.; Hsi-Chieh Lee; Moss, R.H., "Neural network diagnosis of malignant melanoma from color images," IEEE Transactions on Biomedical Engineering, vol.41, no.9 pp.837-845, Sep 1994 | |
| Abstract: | Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images. | |
| 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 | Neural network diagnosis of malignant melanoma from color images | |
| contributor.author | Ercal, Fikret | |
| contributor.author | Chawla, A. | |
| contributor.author | Stoecker, W.V. | |
| contributor.author | Hsi-Chieh Lee | |
| contributor.author | Moss, Randy Hays | |
| contributor.deptlab | Computer Science | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Image Processing Laboratory | |
| subject | 5 y | |
| subject | United States | |
| subject | artificial neural network classification | |
| subject | automated cancer detection | |
| subject | benign tumors | |
| subject | color images | |
| subject | deadliest skin cancer form | |
| subject | discriminant features | |
| subject | malignant melanoma | |
| subject | medical diagnostic images | |
| subject | medical image processing | |
| subject | neural network diagnosis | |
| subject | patient diagnosis | |
| subject | relative tumor color | |
| subject | skin | |
| subject | skin tumor images | |
| subject | tumor shape | |
| date.issued | 1994 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Ercal, F.; Chawla, A.; Stoecker, W.V.; Hsi-Chieh Lee; Moss, R.H., "Neural network diagnosis of malignant melanoma from color images," IEEE Transactions on Biomedical Engineering, vol.41, no.9 pp.837-845, Sep 1994 | |
| identifier.issn | 0018-9294 | |
| identifier.pub.URI | ||
| description.abstract | Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images. | |
| 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 | 2007-04-05T13:58:06Z | |
| date.available | 2007-04-05T13:58:06Z | |
| identifier.persist.URI | ||
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