eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-09-19 18 October Spl Edition 67577 article Rekha Sharmily Raja Dhurai 1 Karthik Balaguru 1 Vijayan Thiruvengadam 1 Department of ECE, Bharath Institute of Higher Education and research, Chennai, India Brain tumors are malignant neoplasms characterized by one of the lowest survival rates. Deep learning, an established and vigorous machine learning technology, has been extensively employed in numerous applications to address complicated challenges demanding exceptional accuracy in the medical domain. Classifying a brain tumor is an essential step after its identification to formulate an effective treatment strategy. The primary goal of this research is to build a framework for the automatic classification of brain tumor with less computation time. Three classifiers namely, Inception V3, Xception, and EfficientNetB3 were utilized, incorporating a callback function along with L1 and L2 regularization to optimize the models, maintain sparsity, and preserve key features. The three models exhibited impressive outcomes, however the Xception network displayed the higher accuracy of 98.78%, precision, recall and F1 measure of 98.33%, 99% and 98.67%. The outcomes are compared with various previous studies and state-of-the-art techniques conducted using the same dataset applied in our work. The metrics reveal that, the suggested research provides valuable insights toward automatically classifying brain tumors with fewer computational units and time. In future this model can be interpreted with unknown dataset. https://biomedpharmajournal.org/vol18octoberspledition/an-enhanced-framework-leveraging-pre-trained-cnn-models-for-brain-tumor-classification-in-mri-scans/ Brain Tumor Deep learning Efficientnet B3 Inception V3 MRI Xception