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 Deep Learning-Based Approach for Automatic Detection of Malaria in Peripheral Blood Smear Images
Tác giả hoặc Nhóm tác giả: Vu-Thu-Nguyet Pham, Quang-Chung Nguyen, Quang-Vu Nguyen, and Huu-Hung Huynh
Nơi đăng: Lecture Notes in Network and Systems series (Springer)/; Số: 734;Từ->đến trang: 114-125;Năm: 2023
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Malaria is a deadly disease that affects millions of people around the world every year. An accurate and timely diagnosis of malaria is essential for effective treatment and control of the disease. In this study, we propose a deep learning-based approach for automatic detection of malaria in peripheral blood smear images. Our approach consists of two stages: object detection & binary classification using Faster R-CNN, and multi-class classification using EfficientNetv2-L with SVM as the head. We evaluate the performance of our approach using the mean average precision at IoU = 0.5 (mAP@0.5) metric. Our approach achieves an overall performance of 88.7%, demonstrating the potential of deep learning based approaches for accurate and efficient detection of malaria in peripheral blood smear images. Our study has several implications for the field of malaria diagnosis and treatment. The use of deep learning-based approaches for malaria detection
could significantly improve the accuracy and speed of diagnosis, leading to earlier and more effective treatment of the disease.
ABSTRACT
Malaria is a deadly disease that affects millions of people around the world every year. An accurate and timely diagnosis of malaria is essential for effective treatment and control of the disease. In this study, we propose a deep learning-based approach for automatic detection of malaria in peripheral blood smear images. Our approach consists of two stages: object detection & binary classification using Faster R-CNN, and multi-class classification using EfficientNetv2-L with SVM as the head. We evaluate the performance of our approach using the mean average precision at IoU = 0.5 (mAP@0.5) metric. Our approach achieves an overall performance of 88.7%, demonstrating the potential of deep learning based approaches for accurate and efficient detection of malaria in peripheral blood smear images. Our study has several implications for the field of malaria diagnosis and treatment. The use of deep learning-based approaches for malaria detection
could significantly improve the accuracy and speed of diagnosis, leading to earlier and more effective treatment of the disease.
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