Brain tumor classification for MR images using transfer learning and fine-tuning.

Accurate and precise brain tumor MR images classification plays important role in clinical diagnosis and decision making for patient treatment. The key challenge in MR images classification is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional machine learning techniques for classification focus only on low-level or high-level features, use some handcrafted features to reduce this gap and require good feature extraction and classification methods.

Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles.

Membrane protein is a pivotal constituent of a cell that exerts a crucial influence on diverse biological processes. The accurate identification of membrane protein types is deeply essential for revealing molecular mechanisms and drug development. Primarily, several traditional methods were exploited to classify these types.