System for efficient uterus cancer segmentation and classification using deep learning, identifies nutrient deficiency in plants using convolution neutral networks and uses algorithms to make optimal use of projects for output image with accurate process
	HABIBA U H; GOUD B M; THIRUPATHI B; VENKATESH B; PRANAY B; HEMALATHA B
	2023-10-16
专利权人	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
申请日期	2023-10-16
专利号	IN202341069722-A
成果简介	NOVELTY - The system performs acquisition process for collection of images. The system performs refined search on the dataset of cervical cancer and obtains images, where image preprocessing includes converting RBG images into Grayscale images using Python(RTM: Programming language). The RGB image is presented with original colors and the GrayScale images comprise the combination of black and white, where conversion of the images to GrayScale helps in improving the accuracy of results. The system identifies nutrient deficiency in plants using convolution neutral networks and uses algorithms such as TensorFlow , karas, and Deep learning to make the optimal use of projects for the output image with accurate process. USE - System for efficient uterus cancer segmentation and classification using deep learning. ADVANTAGE - The system converts the images to gray scale to improve accuracy of the results efficiently, thus improving accuracy of results of the images effectively. The system allows a user to view the images of the cervical cancer in an accurate manner.
IPC 分类号	G06T-007/00 ; G16H-030/40 ; G16H-050/20 ; G16H-050/30
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/19874
专题	中国科学院新疆生态与地理研究所
作者单位	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
推荐引用方式 GB/T 7714	HABIBA U H,GOUD B M,THIRUPATHI B,et al. System for efficient uterus cancer segmentation and classification using deep learning, identifies nutrient deficiency in plants using convolution neutral networks and uses algorithms to make optimal use of projects for output image with accurate process. IN202341069722-A[P]. 2023.

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