fieldid E-Thesis & Research สถาบันเทคโนโลยีไทย-ญี่ปุ่น
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เขตข้อมูล ข้อมูล
บทคัดย่อ
DETECTION OF SEIZURES AND THEIR SIMILARITY IN DOGS USING DEEP LEARNING TECHNIQUE : Seizures in dogs are a prevalent and potentially life-threatening neurological issue and early detection is essential for timely treatment and improved outcomes. The proposed research presents a novel deep learning-based method for automatic seizure detection in dogs using video data. This methodology employs a pose estimation technique using the DeepLabCut software and the ResNet50 model which is trained using supervised learning and extracts the (x y) coordinates of the significant anatomical points on the dogs body from the video frames. These keypoint coordinates are input to different types of Recurrent Neural Network (RNN) architectures including SimpleRNN GRU LSTM and Bidirectional LSTM. The RNN models must categorize the dogs activity as either a seizure or a non-seizure activity (such as sitting lying down walking running or jumping). Experimental results show that the GRU model exhibited the best performance with a high Accuracy of up to 90% and a very significant Recall of up to 100% for seizure detection. This indicates the models ability to detect all instances of seizures completely. It performs better than other RNN models that have been tried and also outperforms traditional methods like SVM which only achieved 82.5% accuracy and a recall of 85%. The result creates the potential for home monitoring systems capable of offering real-time seizure detection and fast automatic alerting to owners even where continuous direct monitoring is not feasible. This signific
ผู้แต่ง
Thanyasiri Nisaika
ประเภทสิ่งพิมพ์
Thesis (MIT) 2024
เลขหน้า
83
หัวเรื่อง
Information Technology--Thesis
หัวเรื่อง
Thai-Nichi Institute of Technology.Information Technology--Thesis