  Brain Tumors are abnormal mass of clustered cells penetrating regions of
brain. Their timely identification and classification help doctors to provide
appropriate treatment. However, Classifi-cation of Brain Tumors is quite
intricate because of high-intra class similarity and low-inter class
variability. Due to morphological similarity amongst various MRI-Slices of
different classes the challenge deepens more. This all leads to hampering
generalizability of classification models. To this end, this paper proposes
HSADML, a novel framework which enables deep metric learning (DML) using
SphereFace Loss. SphereFace loss embeds the features into a
hyperspheric-manifold and then imposes margin on the embeddings to enhance
differentiability between the classes. With utilization of SphereFace loss
based deep metric learning it is ensured that samples from class clustered
together while the different ones are pushed apart. Results reflects the
promi-nence in the approach, the proposed framework achieved state-of-the-art
98.69% validation accu-racy using k-NN (k=1) and this is significantly higher
than normal SoftMax Loss training which though obtains 98.47% validation
accuracy but that too with limited inter-class separability and intra-class
closeness. Experimental analysis done over various classifiers and loss
function set-tings suggests potential in the approach.
