Agro-field Boundary Detection using Mask R-CNN from Satellite and Aerial Images

Kuchkorov, Temurbek; Ochilov, Temur; Gaybulloev, Elyor; Sobitova, Nazokat; Ruzibaev, Ortik

doi:10.1109/icisct52966.2021.9670114

Cited by 6 publications

(1 citation statement)

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“…Adenocarcinomas come in three main pathologic categories: unpredictable, radial, and gastro enteric type. According to the WHO classification, the four main types of GC are tube-shaped, bulge, colloid, and poorly dependable based on the predominant histologic pattern [3]. Stomach cancer prevention is a major public health concern, and early lesion detection is essential for providing a definitive, minimally invasive treatment and enhancing survival rates.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of gastric cancer in role of endoscopic imaging techniques in artificial intelligence and machine learning applications: An overview

K.,

Kanna R.

2024

E3S Web Conf.

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Gastric cancer is a serious medical issue because its occurrence and death rates are increasing all over the world. Furthermore, obesity, tobacco use, alcohol consumption, and a few dietary defense elements are known cancer-causing agents. In some nations, early detection strategies have been shown to reduce GC-related morbidity and mortality. It offers therapies that are minimally invasive like most effective procedure is endoscopic resection. The most appropriate standard for using a procedure that is typically secure to precisely evaluate the lesions region. It is simple method and it can be expected difficult techniques can be viewed as in early stage of tumour in accurate diagnosis. A few uses of computerized method have arisen in the field of gastric malignant growth. For example, image diagnosis-based prediction conclusion and guess expectation, because of its viable computational power and learning capabilities. As a result, a detailed outline of how artificial intelligence can be used to treat gastric cancer through image-based endoscopic diagnosis and machine learning analysis applications this review, which demonstrates the future developments in this field for the early prediction of gastric cancer, it was also thoroughly discussed the possibility of AI models being over fitted, their accuracy, and their usefulness to clinical research in this field of image processing. In addition, in this review article was been detailed about synopsis of the therapy choices of malignant growth.

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Section: Introductionmentioning

confidence: 99%