Follicle Detection Model on Ovarian Ultrasound Image
Every woman has two ovaries. Ovaries have several follicles, which consist of oocytes or eggs which are filled with granulosa cells. Some women can have a difference in the number of follicles in each ovary. There are cases of several follicles that are coincided, making it difficult to calculate th...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/282737/1/Hartati_MIPA.pdf |
| _version_ | 1826050545168678912 |
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| author | Hartati, Sri Musdholifah, Aina Ayu, Putu Desiana Wulaning Dasuki, Jaswadi |
| author_facet | Hartati, Sri Musdholifah, Aina Ayu, Putu Desiana Wulaning Dasuki, Jaswadi |
| author_sort | Hartati, Sri |
| collection | UGM |
| description | Every woman has two ovaries. Ovaries have several follicles, which consist of oocytes or eggs which are filled with granulosa cells. Some women can have a difference in the number of follicles in each ovary. There are cases of several follicles that are coincided, making it difficult to calculate the number of follicles. In this study, the separation of adjoining follicles and automatic follicle counting was carried out from the results of ovarian ultrasound image segmentation. The segmentation results obtained feature information in the form of follicular feature extraction as many as eight features. The techniques used in this work for feature selection was carried out using Principal Components Analysis (PCA) to reduce the feature. In this study, the PCA and Support Vector Machine (SVM) classifier produced higher accuracy than the classification without PCA. The experimental results also show that the proposed method produced higher classification accuracy than previous work, which yielded 90.39% accuracy, 90.27 % sensitivity, and 90.43 % specificity. |
| first_indexed | 2024-03-14T00:05:40Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:282737 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:05:40Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2827372023-11-16T07:03:55Z https://repository.ugm.ac.id/282737/ Follicle Detection Model on Ovarian Ultrasound Image Hartati, Sri Musdholifah, Aina Ayu, Putu Desiana Wulaning Dasuki, Jaswadi Information and Computing Sciences Mathematics and Applied Sciences Every woman has two ovaries. Ovaries have several follicles, which consist of oocytes or eggs which are filled with granulosa cells. Some women can have a difference in the number of follicles in each ovary. There are cases of several follicles that are coincided, making it difficult to calculate the number of follicles. In this study, the separation of adjoining follicles and automatic follicle counting was carried out from the results of ovarian ultrasound image segmentation. The segmentation results obtained feature information in the form of follicular feature extraction as many as eight features. The techniques used in this work for feature selection was carried out using Principal Components Analysis (PCA) to reduce the feature. In this study, the PCA and Support Vector Machine (SVM) classifier produced higher accuracy than the classification without PCA. The experimental results also show that the proposed method produced higher classification accuracy than previous work, which yielded 90.39% accuracy, 90.27 % sensitivity, and 90.43 % specificity. 2023-01-13 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/282737/1/Hartati_MIPA.pdf Hartati, Sri and Musdholifah, Aina and Ayu, Putu Desiana Wulaning and Dasuki, Jaswadi (2023) Follicle Detection Model on Ovarian Ultrasound Image. In: 7th International Conference on Informatics and Computing, ICIC 2022, 8-9 Desember 2022, Denpasar, Bali, Indonesia. https://ieeexplore.ieee.org/document/10006915 |
| spellingShingle | Information and Computing Sciences Mathematics and Applied Sciences Hartati, Sri Musdholifah, Aina Ayu, Putu Desiana Wulaning Dasuki, Jaswadi Follicle Detection Model on Ovarian Ultrasound Image |
| title | Follicle Detection Model on Ovarian Ultrasound Image |
| title_full | Follicle Detection Model on Ovarian Ultrasound Image |
| title_fullStr | Follicle Detection Model on Ovarian Ultrasound Image |
| title_full_unstemmed | Follicle Detection Model on Ovarian Ultrasound Image |
| title_short | Follicle Detection Model on Ovarian Ultrasound Image |
| title_sort | follicle detection model on ovarian ultrasound image |
| topic | Information and Computing Sciences Mathematics and Applied Sciences |
| url | https://repository.ugm.ac.id/282737/1/Hartati_MIPA.pdf |
| work_keys_str_mv | AT hartatisri follicledetectionmodelonovarianultrasoundimage AT musdholifahaina follicledetectionmodelonovarianultrasoundimage AT ayuputudesianawulaning follicledetectionmodelonovarianultrasoundimage AT dasukijaswadi follicledetectionmodelonovarianultrasoundimage |