Review on Different Methods towards Abnormal Mass Detection in Digital Mammogram

Angita Bhattacharje, Sandeep Poddar, Amiya Bhaumik, Dewi Susanna, Andrew Ware

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-breast cancer screening, and primary health cares can introduce this method to the community. The present study objective is to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing
    images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass that have been widely covered in our research work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system.
    Original languageEnglish
    Article number5970
    Pages (from-to)90-96
    Number of pages7
    JournalKesmas: Jurnal Kesehatan Masyarakat Nasional
    Volume17
    Issue numberSpecial Issue 1
    DOIs
    Publication statusPublished - 31 Jul 2022

    Keywords

    • breast cancer
    • computer-aided diagnosis
    • digital mamography
    • future extraction

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