CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK
This paper proposes an efficient Shearlet Based Childhood MedulloBlastoma (SBCMB) detection system. It is a classification system that extracts prominent characteristics for childhood MedulloBlastoma diagnosis from a given collection of histopathological images. Then, those extracted features are us...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
XLESCIENCE
2022-12-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/104 |
| _version_ | 1797938717407576064 |
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| author | Vishal Eswaran Usha Eswaran |
| author_facet | Vishal Eswaran Usha Eswaran |
| author_sort | Vishal Eswaran |
| collection | DOAJ |
| description | This paper proposes an efficient Shearlet Based Childhood MedulloBlastoma (SBCMB) detection system. It is a classification system that extracts prominent characteristics for childhood MedulloBlastoma diagnosis from a given collection of histopathological images. Then, those extracted features are used with specific decision-making algorithms to categorize the histopathological images. After representing the histopathological images by Shearlet, a multiscale framework, Improved Sequential Forward Selection (ISFS) algorithm, is employed to select the dominant feature subset. Finally, Multi-Layer Perceptron (MLP) with ten hidden layers is utilized for melanoma classification. Based on the results of the evaluation of the SBCM system, it seems that the classification may be accomplished exclusively by the ISFS-based features and that the accuracy of the classifier depends on these selected features. The SBCM system provides 97.62% accuracy on 10x magnified images and 98.77% on 100x magnified images for childhood MedulloBlastoma diagnosis. |
| first_indexed | 2024-04-10T19:04:02Z |
| format | Article |
| id | doaj.art-fe7b8a5cc5534a349695dc1c1283d583 |
| institution | Directory Open Access Journal |
| issn | 2457-0370 |
| language | English |
| last_indexed | 2024-04-10T19:04:02Z |
| publishDate | 2022-12-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj.art-fe7b8a5cc5534a349695dc1c1283d5832023-01-31T06:32:25ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702022-12-018291710.29284/ijasis.8.2.2022.9-17132CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORKVishal EswaranUsha EswaranThis paper proposes an efficient Shearlet Based Childhood MedulloBlastoma (SBCMB) detection system. It is a classification system that extracts prominent characteristics for childhood MedulloBlastoma diagnosis from a given collection of histopathological images. Then, those extracted features are used with specific decision-making algorithms to categorize the histopathological images. After representing the histopathological images by Shearlet, a multiscale framework, Improved Sequential Forward Selection (ISFS) algorithm, is employed to select the dominant feature subset. Finally, Multi-Layer Perceptron (MLP) with ten hidden layers is utilized for melanoma classification. Based on the results of the evaluation of the SBCM system, it seems that the classification may be accomplished exclusively by the ISFS-based features and that the accuracy of the classifier depends on these selected features. The SBCM system provides 97.62% accuracy on 10x magnified images and 98.77% on 100x magnified images for childhood MedulloBlastoma diagnosis.https://xlescience.org/index.php/IJASIS/article/view/104medulloblastoma, computerized diagnosis, multiscale framework, histopathological images. |
| spellingShingle | Vishal Eswaran Usha Eswaran CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK International Journal of Advances in Signal and Image Sciences medulloblastoma, computerized diagnosis, multiscale framework, histopathological images. |
| title | CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK |
| title_full | CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK |
| title_fullStr | CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK |
| title_full_unstemmed | CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK |
| title_short | CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK |
| title_sort | childhood medulloblastoma diagnosis using multiscale framework |
| topic | medulloblastoma, computerized diagnosis, multiscale framework, histopathological images. |
| url | https://xlescience.org/index.php/IJASIS/article/view/104 |
| work_keys_str_mv | AT vishaleswaran childhoodmedulloblastomadiagnosisusingmultiscaleframework AT ushaeswaran childhoodmedulloblastomadiagnosisusingmultiscaleframework |