Page 84 - R2017 Final_BE Biomedical Curriculum and Syllabus - REC

Department of BME, REC

UNIT IV DIAGNOSTIC & THERAPEUTIC APPLICATIONS 9
Optical coherence tomography – Optical Elastography - Laser Induced Fluorescence (LIF)-
Imaging - Raman Spectroscopy and Imaging - Holography and speckles - their applications
in biology and medicine

UNIT V SPECIAL TECHNIQUES 9
Photodynamic therapy (PDT) – Applications of PDT- In vitro clinical diagnostics - Near field
imaging of biological structures - fluorescent spectroscopy – Bio-stimulation effect - Laser Safety
Procedures.
TOTAL: 45 PERIODS
OUTCOMES:
On completion of the course students will be able to
• know about optical equipment, their principles, appreciate their usage in therapeutic and
surgical units of the hospitals
• Gain adequate knowledge on fundamentals of tissue optical properties.
• Know about various surgical applications of laser.
• Have in-depth knowledge about diagnostic and therapeutic applications of laser.
• Have sound knowledge about various special optical techniques and imaging modalities.

TEXT BOOKS:
1. Markolf H.Niemz, “Laser-Tissue Interaction Fundamentals and Applications”, Springer,
2007
2. Paras N. Prasad, “Introduction to Biophotonics”, A. John Wiley and Sons, Inc. Publications,
2003

REFERENCES:
1. Leon Goldman, M.D., & R.James Rockwell, Jr., “Lasers in Medicine”, Gordon and Breach,
Science Publishers Inc., 1975.

BM17E11 SOFT COMPUTING METHODS L T P C
3 0 0 3
OBJECTIVES
• To learn the basics of artificial intelligence.
• To learn the theory and implementation of neural networks
• To introduce neural computing as an alternative knowledge acquisition/representation
Paradigm.
• To introduce different optimization techniques.
• To understand fuzzy set theory.

UNIT I INTRODUCTION TO NEURAL NETWORKS 9
Biological Neurons and their Artificial models, Learning and Adaptation, Adaline, Madaline, Single
layer and Multilayer Perceptron, Back Propagation Network, BAM, Hopfield Memory.

UNIT II ADVANCED NEURAL NETWORKS 9
Counter Propagation Network, Feature Mapping, Self Organizing Feature Maps, Learning Vector
Quantization, Support Vector Machines.

UNIT III OPTIMIZATION 9
Derivative-based Optimization – Descent Methods – The Method of Steepest Descent – Classical
Newton‘s Method – Step Size Determination – Derivative-free Optimization.

Curriculum and Syllabus | B.E Biomedical Engineering | R 2017 Page 84