A survey on machine learning based brain retrieval algorithms in medical image analysis

In recent times, researchers showed huge interest in machine learning approaches that attempts to develop the information representations via computational modules. Past decade gained momentum by deep learning approaches and their potential of enhancing the performance for numerous automation operations with superior future research applications. The novelties in medical image processing initialized the unique perspective to diagnose the human body with superior resolution and enhanced accuracy. This paper offers a comprehensive work on existing methodologies that attain optimum results in their respective domains. There exist various Magnetic Resonance Imaging (MRI) brain scan classifiers to obtain efficient features extraction images. The fundamental step in these methods includes several actions to be performed by using different approaches in order to characterize the anomalous developments in MRI scans of brain. Mostly, current techniques are utilizing deep learning feature extraction algorithm from MRI brain scans to obtain their relevant features. Currently, deep learning algorithms associated with medical imaging results in achieving remarkable performance enhancement in diagnosis as well as characterization of complex pathologies in case of brain tumors. This paper provides existing research gaps in identification, segmentation and feature extraction among current approaches. This paper also suggests the future directions to increase the efficiency of current models.