Synopsis

Deep learning can play a vital role in detecting many diseases and health conditions based upon different medical images. Currently, due to various socio-economic factors, diseases have been on a constant rise and it is becoming a matter of pressure for people working in the medical sector to handle all those patients. Manual checkups and diagnosis are usually time consuming and could be erroneous too. Deep learning can offer a significantly more suitable solution to such issues. It can minimize the time of diagnosis, workloads of the people and also ease the path for further investigation. 

Relevance of the Topic

The topic is very closely connected to the real world. This topic has the capability to offer feasible solutions to real life problems, which can eventually play an important role in contributing towards the progress of the medical sector. 

Future Research/Scope

• Build a workable deep learning model with superior accuracy.
• Build a mobile application for easier access.
• Comparison with other state of the art methods.

Skills Learned

• Basics of machine learning.
• Deep learning techniques such as CNN.
• Different CNN architectures such as AlexNet, VGGNet, ResNet, etc.
• Usage of high level neural network APIs such as keras.
• Usage of different libraries such as Numpy, Matplotlib, etc.
• Usage of different model performance metrics such as precision, recall, f1 score, confusion matrix, etc. 

Relevant courses to the topic

• Artificial Intelligence (CSE422)
• Neural Networks (CSE425)
• Machine Learning (CSE427)
• Image Processing (CSE428)

Reading List

• Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I., 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60-88.
• Hesamian, M.H., Jia, W., He, X. and Kennedy, P., 2019. Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32, pp.582-596.
• Ker, J., Wang, L., Rao, J. and Lim, T., 2017. Deep learning applications in medical image analysis. Ieee Access, 6, pp.9375-9389.
• Cai, L., Gao, J. and Zhao, D., 2020. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11).
• Zhang, J., Xie, Y., Wu, Q. and Xia, Y., 2019. Medical image classification using synergic deep learning. Medical image analysis, 54, pp.10-19.