Tích hợp học tự giám sát với thuật toán phân lớp phi tuyến trong kiến trúc Lightweight Swin Transformer để phân lớp hình ảnh X-quang
Abstract
Trong bài báo này, chúng tôi trình bày một phương pháp mới về tích hợp học tự giám sát (SSL) với thuật toán phân lớp phi tuyến trong kiến trúc Lightweight Swin Transformer ( SSLnC-LSwinT) nhằm cải thiện hiệu quả phân lớp ảnh X-quang. Phương pháp của chúng tôi nhằm khai thác dữ liệu chưa được gán nhãn để giải quyết vấn đề khan hiếm dữ liệu có nhãn trong lĩnh vực y tế bằng cách tiếp cận học tự giám sát để học và trích xuất đặc trưng. Một trong những đóng góp quan trọng của chúng tôi là giới thiệu kiến trúc Lightweight SwinT, một biến thể của SwinT với kiến trúc đơn giản hơn, được đề xuất nhằm nâng cao hiệu quả tính toán, giảm độ phức tạp của mô hình và rút ngắn thời gian huấn luyện. Để cải thiện hiệu quả phân lớp, chúng tôi đề xuất tích hợp thuật toán phân lớp phi tuyến thay vì bộ phân lớp tuyến tính trong Lightweight SwinT. Kết quả thực nghiệm nhấn mạnh các đóng góp của chúng tôi, với sự giảm đáng kể thời gian huấn luyện mô hình và cải thiện đáng kể hiệu quả phân lớp. Phương pháp đề xuất, kết hợp SSL dựa trên LSwinT với thuật toán LightGBM, đạt độ chính xác 87%, tăng 1,8% so với phiên bản SwinT không.
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