Abstract
Deep Learning techniques have recently been widely used for medical image analysis, which has shown encouraging results especially for large datasets. In particular, convolutional neural network has shown better capabilities to segment and/or classify medical images like ultrasound and CT scan images in comparison to previously used conventional machine learning techniques. This chapter includes applications of deep learning techniques in two different image modalities used in medical image analysis domain. The application of convolutional neural network in medical images is shown using ultrasound images to segment a collection of nerves known as Brachial Plexus. Deep learning technique is also applied to classify different stages of diabetic retinopathy using color fundus retinal photography.
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Acknowledgements
The authors would like to thank Kaggle for making the ultrasound nerve segmentation and diabetic retinopathy detection datasets publicly available. Thanks to California Healthcare Foundation for sponsoring the diabetic retinopathy detection competition and EyePacs for providing the retinal images.
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Maitra, S., Ghosh, R., Ghosh, K. (2019). Applications of Deep Learning in Medical Imaging. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_6
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