Akademik Veri Yönetim Sistemi
EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES, cilt.28, sa.3, ss.1213-1226, 2024 (SCI-Expanded, Scopus)
Abstract. – OBJECTIVE: In this study, it is
aimed to classify data by feature extraction from
tomographic images for the diagnosis of COVID-19
using image processing and transfer learning.
MATERIALS AND METHODS: In the proposed study, CT images are made better detectable by artificial intelligence through preliminary
processes such as masking and segmentation.
Then, the number of data was increased by applying data augmentation. The size of the dataset contains a large number of images in numerical terms. Therefore, the results of the models are more reliable. The dataset is split into
70% training and 30% testing. In this way, different features of the applied models were found,
and positive effects were achieved on the result.
Transfer Learning was used to reduce training
times and further increase the success rate. To
find the best method, many different pre-trained
Transfer Learning models have been tried and
compared with many different studies.
RESULTS: A total of 8,354 images were used
in the research. Of these, 2,695 consist of
COVID-19 patients and the remaining healthy
chest tomography images. All of these images
were given to the models through masking and
segmentation processes. As a result of the experimental evaluation, the best model was determined to be ResNet-50 and the highest results
were found (accuracy 95.7%, precision 94.7%,
recall 99.2%, specificity 88.3%, F1 score 96.9%,
ROC-AUC score 97%).
CONCLUSIONS: The presence of a COVID-19
lesion in the images was identified with high accuracy and recall rate using the transfer learning model we developed using thorax CT images. This outcome demonstrates that the strategy
will speed up the diagnosis of COVID-19.