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XAI（説明可能なAI）を踏まえた畳込みニューラルネットワークによるトラフグ属の種判別モデルの検討
https://doi.org/10.57348/0002010423
https://doi.org/10.57348/0002010423d3fd7a4d-365f-446b-aacb-7a68e9b701f6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
fish-u_k_72-2_2.pdf
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-07-25 | |||||||||||
| タイトル | ||||||||||||
| タイトル | XAI（説明可能なAI）を踏まえた畳込みニューラルネットワークによるトラフグ属の種判別モデルの検討 | |||||||||||
| 言語 | ja | |||||||||||
| タイトル | ||||||||||||
| タイトル | Species Identification Model of the Tiger Pufferfish Genus using eXplainable Artificial Intelligence(XAI) Based Convolutional Neural Network | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | jpn | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Species Identification; Tigerfish; Convolutional Neural Network; Deep Learning; XAI(eXplainable Artificial Intelligence); Grad-CAM | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | departmental bulletin paper | |||||||||||
| ID登録 | ||||||||||||
| ID登録 | 10.57348/0002010423 | |||||||||||
| ID登録タイプ | JaLC | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 著者 |
石田, 武志
× 石田, 武志
× 芦田, 寛治
× 徳永, 憲洋
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | The Tiger Pufferfish (Takifugu rubripes) is a staple in Japanese cuisine, with over ten species of the Takifugu genus found in the surrounding seas. Given that certain parts of the pufferfish are toxic, they are predominantly prepared by trained professionals. Furthermore, species within the Takifugu genus are susceptible to hybridization, leading to an increase in hybrid numbers. However, identifying these hybrids is a challenging and time-consuming task, even for experts. To address this, we developed a transfer learning model using the pretrained VGG16 model to differentiate between pufferfish species. The VGG16 model, commonly used in image recognition, is built on convolutional neural networks. We also implemented Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretation of the model. Grad-CAM generates a heat map that highlights the areas focused on by the AI model in the image, allowing us to identify factors contributing to misjudgment and make further improvements. We used seven species from the Takifugu genus (excluding hybrids), and approximately 15 colored images of each species were prepared for machine learning. The results showed that our model was able to distinguish between pufferfish species with relatively high accuracy, although some misclassification occurred among species with imilar body patterns. The Grad-CAM results revealed that the model was able to distinguish body patterns, but some misclassifications occurred due to gravel and background objects being recognized as patterns. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : Journal of National Fisheries University ja : 水産大学校研究報告 巻 72, 号 2, p. 39-51, ページ数 13, 発行日 2024-02 |
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| 出版者 | ||||||||||||
| 出版者 | Japan Fisheries Research and Education Agency | |||||||||||
| 言語 | en | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | 水産研究・教育機構 | |||||||||||
| 言語 | ja | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 0370-9361 | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AN00124678 | |||||||||||
| 情報源 | ||||||||||||
| 識別子タイプ | Local | |||||||||||
| 関連識別子 | fish-u_k_72-2_2 | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
