Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 11 1 2025 03 15 15 35 Mahdi Shafiei Neyestanak Tehran University of Medical Science Hamid Jahani Tarbiat Modares University Mohsen Khodarahmi Bahar imaging center Javad Zahiri University of California San Diego Mostafa Hosseini Tehran University of Medical Sciences Amirali Fatoorchi K. N. Toosi University of Technology Mir Saeed Yekaninejad Tehran University of Medical Sciences 2025 02 04 2025 03 15 Purpose: Brain tumors are among the fatal cancers and cause the death of many people annually. Early diagnosis of a brain tumor can help save the patient’s life. Method: We have collected a dataset consisting of 314 brain MRI images in all planes taken by giving a contrast medium with the dimension of 800*512, which offers the highest resolution. First, skull stripping has been implemented to separate the brain from other parts in the images. Next, we have annotated the tumors in the images under the supervision of experienced radiologists to create ground truth. To determine the most effective model versions for all three loss functions, hyperparameter tuning was performed. Following the comparison, the study further evaluates the effectiveness of two loss functions, Binary Cross-Entropy (BCE) and Focal loss, specifically in handling tumor regions within the dataset. Result: The two proposed loss functions were evaluated using 5-fold cross-validation, and the average precision, recall, and f1 were 76.16%, 71.9%, and 74.52 for BCE loss and 82.92%, 79.32%, and 81% for the Focal loss on the test data, respectively. Moreover, the accuracy for BCE loss was 99.03% and 99.44% for the Focal loss. Conclusion: We recommend using BCE loss cautiously in classification tasks without data imbalance and emphasize the adoption of Focal loss for more accurate and reliable results in brain tumor segmentation. https://jbe.tums.ac.ir/index.php/jbe/article/view/1594