Study of a thin applicator for Microwave Ablation of liver malignant tumor

Tal, Nikolay; Oz, Dor; Leviatan, Y.

doi:10.1109/comcas.2015.7360381

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…The classifiers of the bagging-choice trees were used. Appraisal for classification is established by the 86.8% precision of classifier with an affectability of 94.7% and reliability of 0.950AUROC; affectability of 80.0% and insult class of 0.888AUROC; and affectability of 77.8% and questionable class of 0.935AUROC [18]. Computer-assisted identification frameworks can be used by radiologists to allow them to find pneumonia early.…”

Section: Literature Surveymentioning

confidence: 99%

“…The strength and duration of sun exposure, the age of sun exposure, and hence the level of skin pigmentation are all significant factors in assessing hazards. Melanoma rates are frequently noted in countries where transients from northern Europe get a huge amount of brief, true sunshine that the skin of the pioneers does not change, Australia in particular [16][17][18][19][20]. Childhood introduction can be a more important risk factor than adulthood introduction.…”

Section: Introductionmentioning

confidence: 99%

“…False alarms can be caused by seborrheic keratosis when it satisfies some or all of the ABCD criteria. Specialists can distinguish between seborrheic keratosis and melanoma by examination or dermatology [17,18]. However, there is a variation in this model with an addition of E, which means endurance rates (ABCDE).…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

Gupta,

Sharma,

Tyagi

et al. 2023

JAIT

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Melanoma is the most malignant kind of skin cancer, and it is responsible for the majority of deaths caused by skin cancer. However, this can be easily addressed by reverting to the standard method of damage removal if it is discovered in a timely manner. In this view, it is of the utmost importance to develop procedures for the early and reliable identification of melanomas. Since images for melanoma diagnosis are recorded in the clinic as an epiluminance image using a specific kind of equipment, the technique is machine-dependent. We make use of a method for managing images as well as a high-resolution shading image of a skin ulcer captured by a high-resolution camera or other device. Instead of depending on the epilumination images that are produced by the emergency equipment, the initial task that needs to be done in the present research is to capture high-resolution photographs of the skin injuries. The category limit will be determined with the use of machine learning, and then highlight extraction will take place. The research focuses on clinical photos obtained from fast cameras that were taken of individuals suffering from skin cancer. The problem of uneven illumination was kept at a strategic distance by the use of medial separation and pre-processing using the histogram. Utilisation of a brand new image segmentation method called "Otsu" for the extraction of sores. The extraction of ABCD (Asymmetry, Border, Color, and Dimension) involves the use of innovative methodologies as well as the Total Dermoscopic Value in order to characterise the weight coefficients. A solution to the problem of upgrading the categorization error that is based on machine learning. The parameters that are taken into consideration for evaluating the proposed model are senstivity, specificity, precision and accuracy and the results obtained are 1, 0.93, 0.93, and 0.93 respectively. It has been observed that the proposed model performs better as compared to the ones present in existing literature.

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Section: Literature Surveymentioning

confidence: 99%