What is it about?

Through research, the solution based on the use of transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN.

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Why is it important?

The given work based on the YOLOv4-Tiny algorithm can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

Perspectives

State-of-the-art object detection models pre-trained and fine-tuned like the YOLOv4-Tiny can efficiently diagnose brain tumors from MRIs. Compared to classification methods, this work localized brain tumors from the MRIs and classified it specifically with less comprehension required. Unlike segmentation methods, the proposed work can run on most platforms due to the relatively small space requirement and low computational cost. Moreover, compared to existing works that employed bounding box detection methods for meningioma, glioma, and pituitary brain tumors, this work prevailed as the most precise. However, this work still has certain caveats in terms of having bounding boxes to detect tumors. The use of bounding boxes still limits the precise selection of tumors compared to a segmentation approach. With that said, YOLO can capture excess areas due to the complex morphology of tumors compared to the limited shape of the bounding box. Training YOLObased models and other similar models also require lengthy and tedious dataset labeling compared to a classification approach. YOLO can also become sensitive to the lack of data, requiring additional MRI images for future works. Nonetheless, the trade-offs are relatively minimal compared to the emitted solution as YOLO can still scale and evolve through continuous research to resolve the mentioned concerns.

Dr. Francis Jesmar Perez Montalbo
Batangas State University

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This page is a summary of: A Computer-Aided Diagnosis of Brain Tumors Using a Fine-Tuned YOLO-based Model with Transfer Learning, KSII Transactions on Internet and Information Systems, December 2020, Korean Society for Internet Information (KSII),
DOI: 10.3837/tiis.2020.12.011.
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