Automatic Detection and Classification of Liver Lesions from CT-scan Images

Benny, Ria; Thomas, Tessamma

doi:10.1109/icacc.2015.46

Cited by 2 publications

(1 citation statement)

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“…CT is a non-invasive procedure or medical examination that uses an X-ray equipment for producing the cross-sectional images in the parts of a human body [15]. This imposes the requirement to model the automated system for classifying and detecting the liver anomalies based on the images of the CT scan [16]. In the CT scan image, each cross-sectional image specifies the slice of a liver, which is similar to the slice of a bread.…”

Section: Introductionmentioning

confidence: 99%

Atom search‐Jaya‐based deep recurrent neural network for liver cancer detection

Navaneethakrishnan

Vairamuthu

Parthasarathy

et al. 2020

IET Image Processing

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Automatic detection of liver cancer is the fundamental requirement of computer‐aided diagnosis in the clinical sector. The traditional methods used in the liver detection process are not effective in accurately detecting the tumour region using a large‐sized dataset. Moreover, segmenting the large intensity of the tumour region is a complex issue with the existing methods. To overcome these issues, an accurate and efficient liver cancer detection method named atom search‐Jaya‐based deep recurrent neural network is proposed in this research. The proposed method mimics the atomic motion based on the interaction forces and the constraint forces of the hybrid molecules. The optimal solution is revealed through the fitness measure, which in turn accepts the minimal error value as the optimal solution. The weights of the classifier are optimally updated based on the position of the atom with respect to the iterations. The proposed atom search‐Jaya‐based deep recurrent neural network attained significantly better performance in accurately detecting the tumor region using the exploration ability of atoms in the search space. The results obtained by the proposed model in terms of accuracy, specificity, sensitivity, and precision are 93.64%, 96%, 95%, and 94.88%, respectively, while considering the three features using 80% of training data.

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Section: Introductionmentioning

confidence: 99%