Classification of Brain Tumors based on Coherence-based Atoms Correction in Overcomplete Models Learning

Brain tumor detection using MRI imaging has the potential to be greatly improved through the integration of medical knowledge. Solving the problem of brain tumor classification is highly important in the field of medicine as it can greatly impact the effectiveness of treatment options. However, the classification of tumors into Benign or Malignant categories remains a challenging task due to the need for detailed texture analysis and the possibility of errors. Image processing techniques such as dictionary learning-based classifiers can play a critical role in this field. This paper proposes a method that combines textural-statistical features to categorize brain tumors based on employing sparse non-negative matrix factorization (SNMF) and a dictionary learning-based model using a sparse representation technique. In the next step, the extracted features from the sparse coefficient matrix were fed into a ResNet10 model for the classification of the input image. The experimental results emphasize that the proposed method, which trains the dictionary atom based on the combinational features vector, can accurately distinguish different types of brain tumors with high precision. This is a significant method as it can improve the effectiveness of brain tumor classification, leading to more accurate treatment decisions for patients.