Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems

A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods enhancement can be much benefit. In this paper, a convolution neural network-based transfer learning approach utilized mammographic images. The experiments are conducted on ISP MIAS datasets, where dataset used training testing (contrast enhancement). Experimental comparison proposed technique done with most popular direct indirect techniques such as CLAHE, BBHE, RMSHE, stretching. qualitative using mean square error (MSE), signal to noise ratio (SNR), peak (PSNR). It observed that outperforms other HE,