Research on the machine learning with gestalt pattern analysis of drawing picture for exploring novel index of mental disfunctions
| Project/Area Number |
16K01512
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Rehabilitation science/Welfare engineering
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| Research Institution | Kyoto University (2017-2018)
Osaka Prefecture University (2016) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
岩田 基 大阪府立大学, 工学(系)研究科(研究院), 准教授 (70316008)
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| Research Collaborator |
SONODA Tosuke
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2016: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | リハビリテーション / 精神機能障害 / 機械学習 / バウムテスト / 樹木画分類 / ゲシュタルト / 社会系心理学 |
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| Outline of Final Research Achievements |
The classification of the gestalt collapse in the tree-drawing is not high in the statistical reliability because of the subjective viewing. For psychiatric additional diagnosis such as delusion or hallucination, the classification of tree-drawing has required objective methods with high accuracy like this Convolutional Neural Network (CNN). We aim to construct the classifier by using CNN. In this research, we propose methods to improve the accuracy of the classifier of tree-drawings using CNN. The present study confirmed that the classification of tree-drawings using CNN has improved the accuracy well, and suggests the possibility of useful psychiatric additional diagnosis in future clinical setting.
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| Academic Significance and Societal Importance of the Research Achievements |
現在のバウム画におけるゲシュタルト形成不全は、目視による主観的な判定方法のため、評価者の経験値と熟練度によって再現性を保つことができない。このため、客観的に分類できる補助的な判定法の確立が模索されている。本研究では、畳み込みニューラルネットワーク(CNN)における構造モデルを検討した結果、「SE-ResNet-34」における全体の推定精度が69.7%と最も高いことを確認した。これは、幻覚・妄想といった精神症状の関連が示唆されるゲシュタルトの統合度が低下したバウム画を客観的に分類できる画型判定法の確立に向けた出発点になる研究であると位置づけられる。
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Report
(4 results)
Research Products
(8 results)
