Abstract

Breast cancer is one of the leading causes of death in women around the world. Mammography is the most common screening method for an early finding of breast cancer. The major goal of mammography is to identify tiny lumps as soon as possible because they might lead to cancer. However, standard mammography makes it difficult to detect very small tumours. Image processing technologies in digital mammography methods permit for modification of small dissimilarities in image contrast. Enhancement is used to bring out specific mammographic aspects such as mass and micro-calcification, as well as to highlight certain characteristics for early identification and diagnosis of breast cancer. Increasing the contrast of a medical image is a crucial first step in the analysing process. The contrast and brightness preservation of digital mammograms with the high contrast-limited adaptive histogram equalisation (HCLAHE) method is improved. This research checks the proposed strategy against well-established methods like HE (histogram equalisation), AHE (adaptive histogram equalisation), and CLAHE (contrast-limited adaptive histogram equalisation). It has a significant impact on the characteristic of minimising the absolute mean brightness error between input and output images, and it displays increased entropy to extract possible information from variables to reduce visualisation difficulties. The results reveal that using the proposed methodology can outperform in terms of contrast enhancement and brightness preservation in a digital mammogram.

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