Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation
This work addresses the problem of timely brain tumor detection for medical diagnosis, but it is incremental as it builds on existing transfer learning methods with a new dataset and minor architectural enhancements.
The paper tackled brain tumor detection from MRI scans by proposing a deep-learning framework using transfer learning and a novel Dense-Dropout sequence, achieving improved robustness and reduced overfitting, and introduced the MMCBT dataset with 3671 tumor and 13273 non-tumor images to address data scarcity.
Brain tumors are abnormal cell growths in the central nervous system (CNS), and their timely detection is critical for improving patient outcomes. This paper proposes an automatic and efficient deep-learning framework for brain tumor detection from magnetic resonance imaging (MRI) scans. The framework employs a pre-trained ResNet50 model for feature extraction, followed by Global Average Pooling (GAP) and linear projection to obtain compact, high-level image representations. These features are then processed by a novel Dense-Dropout sequence, a core contribution of this work, which enhances non-linear feature learning, reduces overfitting, and improves robustness through diverse feature transformations. Another major contribution is the creation of the Mymensingh Medical College Brain Tumor (MMCBT) dataset, designed to address the lack of reliable brain tumor MRI resources. The dataset comprises MRI scans from 209 subjects (ages 9 to 65), including 3671 tumor and 13273 non-tumor images, all clinically verified under expert supervision. To overcome class imbalance, the tumor class was augmented, resulting in a balanced dataset well-suited for deep learning research.