This document summarizes a PHD research review meeting discussing the use of machine learning algorithms for medical image analysis and diagnosis. It discusses how machine learning can help classify medical images and predict disease. Specific machine learning models and techniques are mentioned, like convolutional neural networks, decision trees, and reinforcement learning. Applications to tasks like segmentation, classification, and registration of medical images are covered. Several medical image databases and software libraries for deep learning are also listed. The conclusion covers opportunities for improving accuracy, scalability, and using recurrent neural networks to model image sequences and predict disease progression.

1. PHD RESEARCH REVIEW MEETING
Supervisor: Dr.Amit Kumar Goel
SCSE Department
Galgotias University

2. INTRODUCTION
 Images are primary tool of diagnosis in healthcare
sector
 Machine Learning can play prominent role in
analysis of medical images
 Using computer to analyze medical data is known
as Computer Aided Diagnosis
 Machine learning algorithm can help to classify and
predict disease

3. COMPUTER AIDED DIAGNOSIS

4. MACHINE LEARNING

5. MACHINE LEARNING ALGORITHM
 In supervised learning we divide data into training
data and testing data
 Training data is used to identify the features which
are used to classify test data
 In unsupervised learning machine learns the
features on its own
 In reinforcement learning algorithm tries to optimize
cost function

6. MACHINE LEARNING MODEL
 Convolutional neural network
 Decision Tree
 Support Vector machine
 Regression Analysis
 AutoEncoder
 Linear and logistic classifier

7. APPLICATION IN MEDICAL DIAGNOSIS
 The most popular task in healthcare sector is
segmentation ,classification and registration
 CNN is most popular tool to classify the image into
malignant and benign
 Auto Encoder has been used to solve classification
problem
 Reinforcement learning algorithm has been used
along with CNN to classify lung cancer image
classification

8. APPLICATION IN MEDICAL DIAGNOSIS
 Decision tree has been used to classify CT images
, breast cancer images etc.
 Support vector machines to classify breast tissue
as malignant
 Recurrent neural network has been used to analyze
EHG signals


9. SOFTWARE
 Caffe:C++ and python deep learning library
maintained UCBerkley
 Tensor Flow :Google’s deep learning library
 Keras:Python based deep learning library
 Scikit-learn:Python based machine learning library
 PyTorch:Python based machine learning library

10. IMAGE DATABASE
 NLM ‘s MedPix
database:https://medpix.nlm.nih.gov/home
 The Cancer Imaging Archive
https://www.cancerimagingarchive.net/
 NIH Database of 100,00 chest X-ray
https://nihcc.app.box.com/v/ChestXray-NIHCC
John Hopkins Medical Institute Database
http://lbam.med.jhmi.edu/

11. IMAGE DATABASE
 SICAS medical image repository
https://www.smir.ch/
 MIDAS
https://www.insight-journal.org/midas/
 National Alliance for Medical Image Computing
https://www.insight-
journal.org/midas/community/view/17

12. CONCLUSION
 Medical Diagnosis is an emerging field and there
are lot of scopes for improvisation
 100% accuracy in classification has not been
achieved in almost all cases
 Scalability is another issues that has to be verified
 Computation time of ML algorithms are very high

13. CONCLUSION
 Recurrent neural network has not been much used
in the classification of medical images
 Recurrent neural network is more efficient in
handling dependent data
 Medical images can be model as sequence of
images
 Based on image history next stage of disease can
be predicted

14. REFERENCES
 [1] Yuxi Li.Deep Reinforcement Learning: An
Overview.arXiv:1701.07274, 2017.
 [2]Computer-aided diagnosis.Wikipedia.
https://en.wikipedia.org/wiki/Computer-aided_diagnosis
 [3]Brody, H. Medical imaging. Nature 502, S81 (2013).
https://doi.org/10.1038/502S81a
 [4]. F. Sahba, H. R. Tizhoosh and M. M. A. Salama, "A Reinforcement
Learning Framework for Medical Image Segmentation," The 2006 IEEE
International Joint Conference on Neural Network Proceedings,
Vancouver, BC, 2006, pp. 511-517doi: 10.1109/IJCNN.2006.246725
 [5] Netto, Stelmo & Leite, Vanessa & Silva, Ari & Paiva, Anselmo & Neto,
Areolino. (2008). Application on Reinforcement Learning for Diagnosis
Based on Medical Image. 10.5772/5291.
 [6] F. Ghesu et al., "Multi-Scale Deep Reinforcement Learning for Real-
Time 3D-Landmark Detection in CT Scans," in IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 41, no. 1, pp. 176-189, 1
Jan. 2019.
 [7] Wang L, Chen KC, Gao Y, Shi F, Liao S, et al. 2014. Automated bone
segmentation from dental CBCT images using patch-based sparse
representation and convex optimization. Med. Phys. 41:043503