スマート・オステオロジー: 獣医学解剖学教育と法医学アプリケーションのためのYOLOv5とCNNアーキテクチャを使用した多種長骨検出と分類のためのAI駆動の2段階システム
1Department of Anatomy, Faculty of Veterinary Medicine, Erciyes University, 38039 Kayseri, Türkiye.
Veterinary sciences
|August 28, 2025
まとめ
この研究では,獣医および法医学における正確な骨検知と種分類のために,YOLOとCNNを用いたAIシステムを開発しました. このモデルは97.6%の精度を達成し,安全なデータ処理のためのオフラインのモバイル機能を提供します.
科学分野:
- 獣医の解剖学
- 法医科学
- 人工知能
背景:
- 骨の種類と種を正確に特定することは 獣医の解剖学,法医学,考古学において極めて重要です.
- 既存の方法は,さまざまなフィールドアプリケーションに必要な速度,精度,またはアクセシビリティが欠けている可能性があります.
研究 の 目的:
- 骨の自動検知と分類のためのAI駆動システムの開発と検証.
- 獣医学教育,法医学調査,考古学研究におけるAIの適用性を高める.
主な方法:
- 牛,馬,犬の骨の画像に YOLOアルゴリズムを利用した.
- 骨の種類と種の分類のためにコンボリューションニューラルネットワーク (CNN) を採用した.
- 26,148枚のデータセットで モデルを訓練した
主要な成果:
- 骨の検出と分類において高精度で最大97.6%を達成した.
- モバイル端末とオフラインで利用可能なシステムを開発し,データセキュリティを強化しました.
- ユーザーインタラクション (テキスト/スピーチ) と PDF 出力のための仮想アシスタントを統合しました.
結論:
- AIシステムは 獣医学解剖学教育,法医学,考古学,犯罪現場調査における デジタル変革の大きな可能性を 示しています
- システムの精度,セキュリティ機能,ユーザーフレンドリーなインターフェースは,重要な分野での実用的なアプリケーションをサポートします.
- この研究は,AI主導の法医学と解剖学的分析の将来の進歩のための基礎を築きます.
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