Optimized Deep Learning Framework Utilizing DenseNet121 for High-Accuracy Image Classification with Improved Computational Efficiency and Feature Learning

In neuro-oncology, accurate and quick diagnosis of brain tumors is crucial and greatly affects patient survival outcomes and treatment approaches. Based on MRI, the proposed method offers an optimal deep learning framework using DenseNet121 architecture to classify brain cancers. DenseNet121 differs from conventional CNN-based techniques in that it makes use of densely linked convolutional layers, which support less computational redundancy, effective gradient flow, and enhanced feature reuse. These features greatly improve classification accuracy and help to solve usually related to deep network vanishing gradient issues. The proposed system runs thorough preprocessing, refines a pre-trained DenseNet121 model using an MRI brain scan dataset by using data augmentation approaches, hence enhancing model generalization. The Densnet121 has validation accuracy of 86% and 0.9847 AUC score, beats all of the leading models including InceptionV3, VGG19, ResNet50, Custom CNN, and EfficientNet B0. The approach preserves computing efficiency while displaying resilience in separating tumor from non-tumor conditions. The results highlight DenseNet121's scalability and therapeutistic feasibility as a suitable solution for automated brain tumor identification.