This is about digital image processing

1. MAHENDRA INSTITUTE OF
TECHNOLOGY
PHASE-0
GUIDED BY PRESENTED BY
Ms.G.NIRMALA (AP/ECE) N.GAYATHRI
S.GOVARTHINI
P.KRITHIKA
P.NANDHINI

2. DOMAIN
DIGITAL IMAGE PROCESSING

3. INTRODUCTION
• In computer science, digital image processing is the use of a digital computer to
process digital image through an algorithm.
• It allows a much wider range of algorithms to be applied to the input data and
can avoid problems such as the bulid up of noise and distortion during
processing.
• It improve the visual quality of an image and the distribution of intensity.

4. TITLE
HUMAN LIFE TIME PREDICTION AND SURVIVAL
ANALYSIS ON BREAST CANCER USING DEEP
NEURAL NETWORK

5. ABSTRACT
• Improved cancer prognosis is a central goal for precision health medicine. In turn,
these models should provide deeper insight into which types of data are most
relevant to improve prognosis. Deep Learning-based neural networks offer a
potential solution for both problems because they are highly flexible and account
for data complexity in a non-linear fashion. We accomplish this through an
algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural
Networks), which aggregates and simplifies gene expression data and cancer
biomarkers to enable prognosis prediction. The results revealed improved
performance when more omics data were used in model construction.

6. BLOCK DIAGRAM

7. EXISTING METHODOLOGY
• In existing methodology shows the feasibility of discovering breast cancer
related co-expression modules,sketch a blueprint of future endeavors on deep
learning based survival analysis.

8. PROPOSED METHODOLOGY
SEGMENTATION
CONVOLUTIONAL NEURAL NETWORK
CLASSIFICATION
DEEP NEURAL NETWORK

9. HARDWARE & SOFTWARE
HARDWARE REQUIREMENTS
PC
SOFTWARE REQUIREMENTS
MATLAB/PYTHON

10. REFERENCE
• Arash, E. H., Shiban, A., Song, S. Y., and Attisano, L. (2017). MARK4 inhibits
Hippo signaling to promote proliferation and migration of breast cancer
cells. EMBO Rep. 18, 420–436.
• Barry, W. T., Kernagis, D. N., Dressman, H. K., Griffis, R. J., Hunter, J. D.,
Olson, J. A., et al. (2010). Intratumor heterogeneity and precision of microarray-
based predictors of breast cancer biology and clinical outcome. J. Clin.
Oncol. 28, 2198–2206.