Transactions on Machine Intelligence 2821-1693 7 4 2024 12 01 Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning 269 275 205121 10.47176/TMI.2024.269 EN M. M. Haj Hashem Khani Department of Electrical Engineering (Electronics), South Tehran Branch, Islamic Azad University, Tehran, Iran F. Maleki Nodehi Department of Electrical Engineering (Electronics), South Tehran Branch, Islamic Azad University, Tehran, Iran Journal Article 2024 07 13 In this study, a deep learning-based brain tumor detection model is proposed using a Convolutional Neural Network (CNN) architecture, specifically the ResNet18 model. The aim is to develop an automated and accurate system capable of detecting brain tumors from MRI images, classifying them into two categories: “tumor present” and “no tumor.” To enhance performance and reduce the need for large-scale annotated medical datasets, the model employs transfer learning by initializing with pre-trained weights from the ImageNet dataset. The final fully connected layers of the ResNet18 network are fine-tuned to adapt to the specific binary classification task. The MRI dataset is divided into training and test sets, and preprocessing steps such as image resizing and normalization are applied to standardize inputs. After training for ten epochs, the model achieved promising results, including an accuracy of 84.31%, a precision of 79.31%, a recall of 92.00%, and an F1 score of 85.19%. These metrics indicate the model’s robustness in detecting tumors with high sensitivity and specificity. The experimental results suggest that the proposed method can effectively extract and interpret critical features from MRI scans, offering a reliable tool for assisting radiologists in early diagnosis and reducing the risk of human error in clinical decision-making. https://www.tmachineintelligence.ir/article_205121_86fe9cf2ee0ddd669a4565465586de21.pdf