Akademik Veri Yönetim Sistemi
Engineering Sciences, cilt.17, sa.3, ss.35-41, 2022 (Hakemli Dergi)
Today, tick-borne diseases have become widespread and pose a
significant threat to public health. There are various types of diseases
transmitted from ticks to humans. The main of these diseases; CrimeanCongo hemorrhagic fever, Lyme disease, Mediterranean spotted fever and
Tularemia can be listed. As with other types of diseases, early diagnosis
is important in tick-borne diseases. Therefore, it is necessary to
identify ticks quickly and accurately in order to reduce the possible
risks of disease in cases of errors bitten by ticks. In this study,
Yolo-v3-based deep learning algorithm, which is a subfield of machine
learning, was used primarily to detect ticks. For the training and
testing of this algorithm, a new data set was created by downloading
1500 different tick images from the internet. Algorithm was trained and
tested using this data set. In order to determine that the success
accuracy of the Yolo-v3-based deep learning algorithm is superior and
to demonstrate its availability in real life, various performance tests
were performed and an estimate was made as to whether there were ticks
in an image. As a result of the study, in order to reduce the disease
risk of patients bitten by ticks and to intervene in a timely manner,
tick detection was made effectively by taking only one tick picture.