Towards Efficient for Learning Model Image Retrieval

Ghrabat, Mudhafar Jalil Jassim; Ma, Guangzhi; Cheng, C. H.

doi:10.1109/skg.2018.00020

Cited by 3 publications

(1 citation statement)

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“…The suggested procedure may be used in the assessment of cases featuring several cancers, even where these are represented in separate medical videos. The proposed method of segmenting medical images and properly identifying cancer has the potential to provide urgently required details regarding the structure of tumours [33]. This research also used a variety of approaches simultaneously to develop a practical technique for dissemination.…”

Section: Proposed Methodsmentioning

confidence: 99%

Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Abduljabbar

2023

IJCAI

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One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.

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Section: Proposed Methodsmentioning

confidence: 99%