stdClass Object
(
    [id] => 5028
    [paper_index] => 202105-02-007055
    [title] => AN ENHANCED METHOD OF LIVER LESION DETECTION USING DEEP NEURAL NETWORK, WATERSHED TRANSFORM AND GAUSSIAN MIXTURE MODEL TECHNIQUES IN MR IMAGES
    [description] => 
    [author] => A. BathshebaParimala,R.S.Shanmugasundaram 
    [googlescholar] => https://scholar.google.co.in/scholar?scisbd=2&q=eprajournals.com&hl=en&as_sdt=0,5
    [doi] => https://doi.org/10.36713/epra7055 
    [year] => 2021
    [month] => May
    [volume] => 6
    [issue] => 5
    [file] => 108am_64.EPRA JOURNALS-7055.pdf
    [abstract] => Cancer of the liver is one of the leading causes of death all over the world. Physically recognising the malignancy tissue is a difficult and time-consuming task. In the future, a computer-aided diagnosis (CAD) will be used in dynamic movement to determine the precise position for care. As a result, the primary goal of this research is to use a robotized approach to precisely identify liver cancer. Methods: In this paper, we suggest a new approach called the watershed Gaussian based deep learning (WGDL) strategy for accurately portraying malignant growth sores in liver MRI images. This project used a total of 150 images to build the proposed model. The liver was first isolated using a marker-controlled watershed division scale, and the malignancy-induced injury was then divided using the Gaussian mixture model (GMM) algorithm. Different surface highlights were removed from the sectioned locale after tumour division. These jumbled highlights were fed into a deep neural network (DNN) classifier for a computerised classification of three types of liver cancer: haemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET). The following are the outcomes: Using a Deep Neural Network classifier and an unimportant approval deficiency of 0.053 during the characterization period, we were able to achieve a grouping precision of 98.38 percent at 150 ages. The system in our proposed approach is suitable for testing with a large data set and can assist radiologists in detecting liver malignant growth using MR images.
    [keywords] => computer-aided diagnosis (CAD), watershed Gaussian based deep learning, Gaussian mixture model, hepatocellular carcinoma, metastatic carcinoma, Deep Neural Network classifier.
    [doj] => 2021-05-23
    [hit] => 1964
    [status] => y
    [award_status] => P
    [orderr] => 64
    [journal_id] => 2
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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