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In the field of medical imaging, there is a strong requirement for the storage of an immense volume of digitized medical image data. The digital image must be compressed heavily before storing and transferring it because of having restricted bandwidth and scope of storage. When compression of images is done at a lower bit rate it reduces the image fidelity that results in a drop in quality but poses many challenges to overcome and prevents diagnostic miscalculations with great compression rates for reduced storage and quick transmission. To overcome this challenging issue several hybrid efficient compression procedures solely for medical digital images have been introduced in recent years. The transformation of image, quantization, and encoding is part of image compression. This paper presents a qualitative and comprehensive review of image compression techniques for two-dimensional (2D) still and three-dimensional (3D) medical images. The features and constraints associated with various compression methods for compressing grayscale images are reviewed and discussed in this paper. In-depth reviews of the practical concerns and difficulties in the medical scan compression arena are provided.

This research conducts transfer learning with optimal training option identification for the detection of wrist bone abnormalities in X-Ray imagery. Specifically, transfer learning based on Convolutional Neural Networks (CNNs), such as ResNet-18 and GoogLeNet, has been developed for wrist bone abnormality detection. The effect of altering the number of epochs on the network performance using an automatic process is also investigated. The MURA wrist radiological images are extracted in our experiments. The proposed system achieves a superior performance for wrist bone abnormality detection in comparison with those of existing studies.