  Histopathological images (HI) encrypt resolution dependent heterogeneous
textures & diverse color distribution variability, manifesting in
micro-structural surface tissue convolutions. Also, inherently high coherency
of cancerous cells poses significant challenges to breast cancer (BC)
multi-classification. As such, multi-class stratification is sparsely explored
& prior work mainly focus on benign & malignant tissue characterization only,
which forestalls further quantitative analysis of subordinate classes like
adenosis, mucinous carcinoma & fibroadenoma etc, for diagnostic competence. In
this work, a fully-automated, near-real-time & computationally inexpensive
robust multi-classification deep framework from HI is presented.
  The proposed scheme employs deep neural network (DNN) aided discriminative
ensemble of holistic class-specific manifold learning (CSML) for underlying HI
sub-space embedding & HI hashing based local shallow signatures. The model
achieves 95.8% accuracy pertinent to multi-classification & 2.8% overall
performance improvement & 38.2% enhancement for Lobular carcinoma (LC)
sub-class recognition rate as compared to the existing state-of-the-art on well
known BreakHis dataset is achieved. Also, 99.3% recognition rate at 200X & a
sensitivity of 100% for binary grading at all magnification validates its
suitability for clinical deployment in hand-held smart devices.
