(Institut Kesehatan dan Teknologi Al-Insyirah, Indonesia)
(2) Romi Mulyadi
(Institut Kesehatan dan Teknologi Al-Insyirah, Indonesia)*Corresponding author
AbstractBrain cancer is one of the diseases with a high mortality rate that requires early detection to increase the effectiveness of treatment. This study proposes a brain cancer detection system based on MRI images by utilizing the Convolutional Neural Network (CNN) algorithm for classification and You Only Look Once (YOLO) for cancer location detection. The MRI dataset was taken from the Kaggle platform and processed through the normalization stage and CNN model training for 20 epochs. The CNN performance evaluation resulted in an accuracy of 94.95%, precision of 93.11%, recall of 89.11%, and F1-score of 91.07%. Furthermore, the YOLO model was used to identify the location of cancer with high visual accuracy. This system was also tested using new images with the results of detecting the location of cancer in an average time of 8.3 seconds for 4 images. The results of the study indicate that the combination of CNN and YOLO can be an effective solution in an automatic, accurate, and fast brain cancer detection system, as well as providing visual support for medical personnel in the diagnosis process.
KeywordsBrain cancer, Convolutional Neural Networks (CNN), Yolo, MRI, Deep Learning, Location Detections
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DOIhttps://doi.org/10.33122/ejeset.v6i2.926 |
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