eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-10-20 18 October Spl Edition 68380 article Improving Diagnostic Accuracy in Brain Tumor Detection using EfficientNetB3 Transfer Learning and Support Vector Machine Abhimanu Singh 1 Smita Jain 2 Department of Applied Mathematics, Bhagwan Parshuram Institute of Technology, Delhi, India Department of Mathematics, JECRC University, Jaipur, India Brain tumors create life threatening consequences. Timely and accurate detection of brain tumors is critical for effective treatment planning and improving patient survival chances. In this study, we present a deep learning–hybrid model for automated brain tumor detection using MRI images. Our model consists of two phases. First phase extracts features and the second phase performs classification. EfficientNetB1 convolutional neural network is employed to extract features and Support Vector Machine (SVM) as a classifier. The EfficientNetB1 model, pre-trained on ImageNet, is employed as a fixed feature extractor to leverage its optimized architecture for capturing meaningful spatial representations from medical images. Without the use of data augmentation or dimensionality reduction techniques, the model extracts deep features directly from the MRI images, maintaining the integrity of anatomical information critical for accurate diagnosis. These extracted features are then used to train an SVM classifier with a radial basis function (RBF) kernel, enabling precise binary classification between tumor and non-tumor cases. The proposed method is evaluated on a publicly available brain MRI dataset and achieves a validation accuracy of 99.33%, along with high precision, recall, and F1-score. Compared to conventional CNN-based or transfer learning methods that rely on complex pipelines or augmented data, this architecture remains simple, fast, and highly effective. This framework offers a practical, low-overhead solution for assisting radiologists in brain tumor detection. https://biomedpharmajournal.org/vol18octoberspledition/improving-diagnostic-accuracy-in-brain-tumor-detection-using-efficientnetb3-transfer-learning-and-support-vector-machine/

Brain Tumor Detection Deep Feature Extraction Diagnostic Accuracy Support Vector Machine Transfer Learning