Chẩn đoán tự động mức độ bệnh võng mạc do tiểu đường bằng mô hình học sâu DR-ResNet +

Samiya Majid Baba1, Indu Bala1, Gaurav Dhiman2,3,4,5, Ashutosh Sharma6,7, Wattana Viriyasitavat8
1SEEE, Lovely Professional University, Phagwara, India
2Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
3Centre of Research Impact and Outreach, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
4Division of Research and Development, Lovely Professional University, Phagwara, India
5MEU Research Unit, Middle East University, Amman, Jordan
6Southern Federal University, Rostov-on-Don, Russia
7Business School, Henan University of Science and Technology, Henan, China
8Business Information Technology Division, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand

Tóm tắt

Bệnh võng mạc tiểu đường, một tình trạng vi mạch liên quan đến nguy cơ gia tăng bệnh lý tim mạch, đặt ra một thách thức lớn cho hệ thống y tế toàn cầu. Nhu cầu chẩn đoán kịp thời đã thúc đẩy phát triển các giải pháp tự động do tình trạng thiếu chuyên gia. Trong bài báo này, chúng tôi giới thiệu một phương pháp đột phá trong việc phát hiện bệnh võng mạc tiểu đường - Mạng Residual Bệnh Võng Mạc Tiểu Đường (DR-ResNet +). Mô hình đề xuất tận dụng sức mạnh của học sâu để tự động trích xuất đặc trưng, đạt được kết quả tối ưu chỉ sau bảy lần huấn luyện. Kiến trúc DR-ResNet + được thiết kế một cách tỉ mỉ bằng cách kết hợp một loạt các lớp tích chập, lớp giảm bớt và lớp kết nối đầy đủ. Tối ưu hóa siêu tham số được thực hiện bằng cả hai kỹ thuật tìm kiếm lưới và tìm kiếm ngẫu nhiên để đảm bảo hiệu suất cao nhất. Để xác thực độ ổn định của mô hình đề xuất, các kết quả mô phỏng được so sánh với những mô hình học sâu đã được thiết lập như GoogleNet, VGG16 và AlexNet, sử dụng một tập dữ liệu toàn diện từ Kaggle gồm hơn 35.000 hình ảnh võng mạc. Hơn nữa, mô hình đề xuất cũng được kiểm tra trên các tập dữ liệu bên ngoài như MESSIDOR và IDRiD để xác minh. Kết quả mô phỏng cho thấy mô hình DR-ResNet + không chỉ giảm thời gian huấn luyện tới 95% mà còn thể hiện các chỉ số hiệu suất xuất sắc, bao gồm độ chính xác 0.9898, độ đặc hiệu 0.9916, độ chính xác 0.9670, độ nhạy 0.9829 và điểm F1 0.9748. Những phát hiện này đưa mô hình đề xuất trở thành một lựa chọn hết sức phù hợp cho các ứng dụng lâm sàng theo thời gian thực, mang tới một tiềm năng thay đổi đáng kể trong chẩn đoán bệnh võng mạc tiểu đường. Bài báo này trình bày DR-ResNet + như một bước tiến tiên phong trong chẩn đoán bệnh võng mạc tiểu đường. Với sự huấn luyện nhanh chóng, độ chính xác vượt trội và những ảnh hưởng rõ rệt tới thực tiễn, mô hình này có tiềm năng biến đổi lĩnh vực chăm sóc sức khỏe bằng cách cung cấp các chẩn đoán kịp thời và chính xác cho tình trạng bệnh lý quan trọng này.

Từ khóa

#bệnh võng mạc tiểu đường #học sâu #mô hình DR-ResNet + #chẩn đoán tự động #y tế

Tài liệu tham khảo

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