Deep Learning-Based Medical Image Analysis Using Transfer Learning

There is a wide spectrum of different deep learning (DL) architectures available for medical image analysis. Among this convolution networks (CNN) found to be more efficient for variety of medical imaging task including segmentation, object detection, disease classification, severity grading, etc. In medical image analysis, accuracy of prediction is of utmost importance. In machine learning or deep learning, quantity and quality of medical image dataset plays a important role for ensuring the accuracy of future prediction. Otherwise because of less number of poor quality images, machine or deep learning models fail to predict accurately. This limitation of less quantity and less quality medical image dataset is almost removed to major extent by the transfer learning concept of deep learning. Transfer learning concept of deep learning makes the pertained models available for customization to specific application needs. Either pre-trained models are fine-tuned on the underlying data or used as feature extractors. As these pertained models are already trained on large datasets, the accurate set of generic features can be extracted to improve the overall performance and computational complexity. Because of transfer learning, limitation of large dataset requirement is removed to a greater extent and also the training cost in terms of number of parameters to be learned, training time, hardware computing cost is reduced. Plenty of pre-trained models are available including AlexNet, LeNet, MobileNet, GoogleNet, etc. Currently, many researchers are applying DL to obtain promising results in a wide variety of medical image analysis for almost all diseases including all types of cancers, pathological diseases, orthopedic diseases, etc. The proposed chapter covers introduction to deep learning, transfer learning, different award winning architectures for transfer learning, different resources for medical imaging research. This is followed by a brief case study of use of transfer learning for malaria diagnosis. The chapter also highlights on the future research directions in the domain of medical image analysis.