MAFD: Model Agnostic Forest Densenet Approach for Brain Tumor Detection

Medical imaging is vital in diagnosis, treatment planning, and disease monitoring. DenseNet’s dense connections enable effective feature extraction from medical images, capturing local and global patterns with rich contextual information. This capability is crucial in identifying subtle abnormalities, detecting tumors, segmenting organs, and analyzing complex anatomical structures. By leveraging dense connectivity, DenseNet facilitates extracting relevant and discriminative features, enhancing the accuracy and reliability of medical image analysis. Acquiring large-scale annotated datasets can be challenging in the medical domain due to privacy concerns, cost, and limited availability. DenseNet addresses the problem of limited data by effectively leveraging the available samples. The dense connections promote feature reuse, allowing the network to learn from a wider range of data instances, even in scenarios with small or imbalanced datasets. This property enables better generalization and robustness, making DenseNet particularly valuable in medical applications where obtaining extensive labeled data are difficult. In the proposed model, using the concept of transfer learning, the model extracts the features based on the ranking mechanism and works on the extracted features to classify the MRI images as brain tumors. Medical images, such as magnetic resonance imaging (MRI) scans, often consist of high-dimensional data with numerous voxels or pixels. Directly using the raw pixel intensities as input to a machine-learning model can be computationally expensive and may lead to over fitting due to the curse of dimensionality. Feature extraction techniques help reduce the dimensionality of the data by transforming it into a more compact and representative feature space, making the subsequent analysis more efficient and effective.