Abstract:

Abstract Medical imaging becoming an essential life supporting aspect in the current world. It having various types of modalities and each one serves for specific applications. Lot of applications and necessities are there to figure out the various life-threatening diseases. But the identification of abnormalities is not so easy doing manually. It is error prone and time consuming. And also requires lot of proficiency and experience. Deep learning is a state of art methodology which having a huge span of applications especially in medical field. Particularly, in medical imaging the deep learning methods can be applied and can make huge differences in the accuracy of findings. They can be used for synthesis, segmentation, and classification. This study is aimed to focus on the different types of medical imaging modalities and the various deep learning algorithms on medical imaging. The performances of different methods were compared by means of various evaluation metrics.

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