| Presentation | 2020-01-29 [Poster Presentation] Computerized Determination Method for Histological Classification of Breast Masses on Ultrasonographic Images Using CNN Features and Morphological Features Shinya Kunieda, Akiyoshi Hizukuri, Ryohei Nakayama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The purpose of this study was to develop a computerized determination method for histological classifications of masses on breast ultrasonographic images using CNN (Convolutional Neural Network) features and morphologic features in order to assist clinicians in determining a treatment plan. Our database consisted of 585 breast ultrasonographic images obtained from 585 patients. In our proposed method, 1,024 CNN features and eight morphologic features were first determined from a mass. An SVM (Support Vector Machine) with those features was employed to classify among histological classifications of masses. Three-fold cross validation method was used for training and testing the SVM. The classification accuracies of the proposed method were 85.8% (187/218) for invasive carcinomas, 77.1% (54/70) for noninvasive carcinomas, 83.5% (152/182) for fibroadenomas, and 85.2% (98/115) for cysts, respectively. The proposed method yielding high classification accuracies would be useful in the differential diagnosis of masses on breast ultrasonographic images as diagnosis aid. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Histological Classification / Convolutional Neural Network / Mass / Ultrasonographic Image |
| Paper # | MI2019-76 |
| Date of Issue | 2020-01-22 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2020/1/29(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | OKINAWAKEN SEINENKAIKAN |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Chair | Yoshiki Kawata(Tokushima Univ.) |
| Vice Chair | Takayuki Kitasaka(Aichi Inst. of Tech.) / Hidekata Hontani(Nagoya Inst. of Tech.) |
| Secretary | Takayuki Kitasaka(Yamaguchi Univ.) / Hidekata Hontani(Univ. of Hyogo) |
| Assistant | Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] Computerized Determination Method for Histological Classification of Breast Masses on Ultrasonographic Images Using CNN Features and Morphological Features |
| Sub Title (in English) | |
| Keyword(1) | Histological Classification |
| Keyword(2) | Convolutional Neural Network |
| Keyword(3) | Mass |
| Keyword(4) | Ultrasonographic Image |
| 1st Author's Name | Shinya Kunieda |
| 1st Author's Affiliation | Ritsumeikan University(Ritsumeikan Univ.) |
| 2nd Author's Name | Akiyoshi Hizukuri |
| 2nd Author's Affiliation | Ritsumeikan University(Ritsumeikan Univ.) |
| 3rd Author's Name | Ryohei Nakayama |
| 3rd Author's Affiliation | Ritsumeikan University(Ritsumeikan Univ.) |
| Date | 2020-01-29 |
| Paper # | MI2019-76 |
| Volume (vol) | vol.119 |
| Number (no) | MI-399 |
| Page | pp.pp.53-55(MI), |
| #Pages | 3 |
| Date of Issue | 2020-01-22 (MI) |