Abstract:

As science and technology advance in this digital age, digital imaging is growing quickly across all domains. Through digital image processing, the image is transformed into its digital form and subjected to various procedures that yield either an enhanced version of the image or informational characteristics. Image segmentation plays a key part in the various image processing techniques that are accessible. The main purpose of image segmentation is to distinguish interesting items from backgrounds. There are numerous methods for segmenting images. However, a lot of methods occasionally fall short of producing the intended result. Machine learning techniques are used to meet the need, and they work well and produce satisfactory results. This is a brief overview of machine learning methods, with a particular emphasis on artificial neural networks and their advancements in deep learning and convolution neural networks, as well as some information on other methods. Additionally, it provides brief explanations of a few neural networks that are used to segment various medical images, with an emphasis on convolution neural networks, which were created primarily to work with images. The review will give researchers a visual representation and suggestions for how to use these methods further for improved results for segmenting images.

Description:

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