Chẩn đoán lao phổi thứ phát bằng mạng nơ-ron tích chập cải tiến tám lớp với việc đưa vào pooling ngẫu nhiên và tối ưu hóa siêu tham số

Yu-Dong Zhang1,2, Deepak Ranjan Nayak3, Xin Zhang4, Shui-Hua Wang5,6
1School of Informatics, University of Leicester, Leicester, UK
2Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India
4Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Jiangsu Province, Huai’an, China
5School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, UK
6Department of Cardiovascular Sciences, University of Leicester, Leicester, UK

Tóm tắt

Để chẩn đoán lao phổi thứ phát một cách hiệu quả hơn, chúng tôi xây dựng một mạng nơ-ron tích chập cải tiến (ICNN) dựa trên các công nghệ học sâu gần đây. Đầu tiên, một phương pháp gia tăng dữ liệu 12 chiều (DA-12) được đề xuất để tăng kích thước tập huấn luyện. Thứ hai, pooling ngẫu nhiên đã được giới thiệu để thay thế phương pháp pooling trung bình chuẩn và pooling tối đa. Thứ ba, các kỹ thuật chuẩn hóa theo lô và dropout được đưa vào và liên kết với các lớp tích chập và các lớp kết nối đầy đủ, tương ứng. Thứ tư, một tốc độ học động được áp dụng để thay thế tốc độ học tĩnh truyền thống. Thứ năm, tối ưu hóa siêu tham số được sử dụng để tối ưu hóa số lớp bên trong mạng đề xuất. ICNN tám lớp của chúng tôi đã thể hiện kết quả xuất sắc trên tập kiểm tra, với độ nhạy 94,19%, độ đặc hiệu 93,72% và độ chính xác 93,95%. ICNN của chúng tôi cung cấp hiệu suất tốt hơn so với bốn thuật toán tiên tiến khác. Nó có thể giúp các bác sĩ chẩn đoán đưa ra chẩn đoán chính xác hơn về lao phổi thứ phát.

Từ khóa

#lao phổi thứ phát #mạng nơ-ron tích chập #pooling ngẫu nhiên #tối ưu hóa siêu tham số #học sâu

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