An Ensemble Multi Fusion based U-Net with Short Learning Technique for Brain Tumor Classification

Detecting a brain tumor and classifying it into one of many different disease subtypes can be a time-consuming and challenging process. However, by deploying the approach and techniques of the Research Novelty Approach for Brain Tumor Classification presented below, a Multi Fusion based U-Net with Short Learning Technique was created that improves both the efficiency and accuracy of tumor classification for magnetic resonance images (MRIs). The most distinguishing aspect of the model is its ability to adapt quickly to new, rare or previously unseen tumor types, thereby requiring only a few training examples for novel classes. This is achieved by way of few-shot learning, a classification technique in which the model learns to generalize based on a small amount of data, ultimately allowing it to perform well in scenarios where there are few examples per class, a common occurrence in medical imaging. In this instance, only one or few examples of previously unseen tumor types were used. The model achieves fast adaptation to new tumor types by computing prototype representations for each class, capturing the essential characteristics of the class, and it grows more effective as the number of classes increases. Additionally, to stabilize the learning process and facilitate training in an erratic and often noisy large-scale dataset in standard MRI images, the intensity values are not standardized, so there always exists a difference in intensity ranges of different images a normalization equation was used to handle standardizing intensity values across MRIs. Finally, these many different types of tumor classes in MRIs contained a large number of pixels for each image, so that is why a total loss function was used in training that combines the Dice loss and cross-entropy loss into a new loss function, placing special emphasis on pixel outputs, and allowing the model to achieve a precise segmentation and an accurate pixel-wise classification. The end result is a Multi Fusion based U-Net with Short Learning Technique that offers a comprehensive and different solution for brain tumor classification, demonstrating advancements in model adaptation, feature representation, data normalization and loss function optimizatoin, and showing great promise for improving the efficiency and effectiveness of brain-tumor classification, which could in turn help to enhance diagnostics and accelerate medical imaging research. The paper presented a brain tumor classification based on multi fusion based U-Net model with Short Learning. Their proposed novel normalization equation firstly standardizes the MRI images to have zero mean and unit variance, thus stabilizes the learning process in presence of noise or intensity variation. The prototype representations for each class were computed to ensure that the model parameters are adapted through few shots learning and yield good performance in segmenting the brain tumors. The overall loss function is the sum of dice loss and cross entropy loss that increase serious segmentation and proper classification respectively by way of teaching system. In a brain tumor classification, it reached to 96.2% accuracy with precision and recall of 94%, F1-Score of 94% and Dice coefficient is one measure which used in the field of Machine Learning 91 % result between two images where similarity you need such as True Positive or Mutation quantities, improved upto an amazing level.