Developing deep learning algorithms to analyze Raman spectral data for brain cancer diagnosis

Project Title: Developing deep learning algorithms to analyze Raman spectral data for brain cancer diagnosis
Program:
CAREERS (323)

Project Image: {Empty}
Tags:
gpu (80), image-processing (299), machine-learning (272), neural-networks (435), python (69)

Status: Halted
Project Leader
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Project Leader:
Binlin Wu

Email: wub1@southernct.edu
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Project Personnel
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Mentor(s):
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Student-facilitator(s):
Mit Patel (1739)

Mentee(s):
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Project Information
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Project Description:
Raman spectroscopy is an optical molecular diagnostic technique that can be used for label-free in situ non-invasive cancer diagnosis. The traditional methods to analyze Raman spectra are mainly based on the intensities of characteristic peaks that are related to underlying biochemicals. However, due to the high dimensionality, and the complexity of the spectral profiles, it is often difficult and subjective to perform the analysis using the traditional methods and distinguish the spectra for different types of tissues. Using machine learning and deep learning methods to analyze the spectra and classify cancerous tissues can overcome these difficulties. The goal of the project is to use deep learning algorithms such as convolutional neural networks to analyze Raman spectra and distinguish human brain cancers at different grades and normal brain tissues. In the project, we will evaluate different methods to process Raman spectra. For example, we will evaluate different analysis methods such as classification of the spectra with and without pre-processing.

Project Information Subsection
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Project Deliverables:
Working code for the spectral analysis and reasonably good classification outcome using convolutional neural network (CNN).


Project Deliverables:
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Student Research Computing Facilitator Profile:
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Mentee Research Computing Profile:
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Student Facilitator Programming Skill Level: Practical applications
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Project Institution: Southern CT State University
Project Address:
501 Crescent Street
New Haven, Connecticut. 06515

Anchor Institution: CR-Yale
Preferred Start Date: 03/15/2022
Start as soon as possible.: Yes
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 2
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Launch Presentation Date: 04/13/2022
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Wrap Presentation Date: 05/11/2022
Project Milestones:
- Milestone Title: System selection and setup
  Milestone Description: Choose an XSEDE HPC system, set up the platform and working code ready to analyze spectral data.
  Completion Date Goal: 2022-03-31
  Actual Completion Date: 2022-03-25
- Milestone Title: Launch Presentation
  Completion Date Goal: 2022-04-13
- Milestone Title: Develop & Validate Methods
  Milestone Description: Figure out methods to do data augmentation for spectra, and generate preliminary results to show the efficacy of classification between normal tissue and glioma tissues using deep learning (binary classification).
  Completion Date Goal: 2022-04-30
- Milestone Title: Investigate Efficacy of Methods
  Milestone Description: Generate results designed to show the efficacy of classification between normal tissue and glioma tissues, and among different grades of glioma tissues using deep learning with and without preprocessing (e.g. baseline removal, normalization).
  Completion Date Goal: 2022-05-31
- Milestone Title: Wrap Presentation
  Completion Date Goal: 2022-05-11

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What will the student learn?:
Parallel computing, HPC system

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HPC resources needed to complete this project?:
SDSC Expanse Projects Storage, SDSC Dell Cluster with NVIDIA V100 GPUs NVLINK and HDR IB (Expanse GPU). 
Requested through XSEDE portal with application# BIO220038.





Notes:
Student for this project is not being paid by the CAREERS program.



Final Report
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