Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phân đoạn lưỡi tự động sử dụng mô hình mã hóa-giải mã sâu
Tóm tắt
Bài báo này đề xuất một giải pháp phân đoạn lưỡi trong hình ảnh. Giải pháp dựa trên mạng nơ-ron tích chập, sử dụng U-Net sâu với các lớp sâu của các mô-đun mã hóa-giải mã. Mô hình được huấn luyện với độ phân giải khởi đầu là 512 x 512 pixel. Để nâng cao hiệu suất phân đoạn của mô hình đã được huấn luyện trong các môi trường ghi lại khác nhau, ba loại tăng cường dữ liệu chính được thêm vào quá trình huấn luyện, bao gồm tiếng ồn gaussian cộng thêm, nhân và cộng vào độ sáng, và thay đổi nhiệt độ màu. Chúng cũng có thể xử lý số lượng mẫu dữ liệu không đủ trong các tập dữ liệu hạn chế. Phương pháp đề xuất được đánh giá dựa trên bốn chỉ số đo lường là hệ số Dice, IoU trung bình, khoảng cách Jaccard và độ chính xác. Mô hình đã được huấn luyện thành công trên các tập dữ liệu công khai và sau đó được chuyển giao để thử nghiệm với tập dữ liệu tự thu thập trong môi trường thực tế.
Từ khóa
#phân đoạn lưỡi #mạng nơ-ron tích chập #U-Net #tăng cường dữ liệu #đo lường hiệu suấtTài liệu tham khảo
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