Efficient classification of ovarian cancer using CNN and artificial bee colony optimization for research in ovarian cancer identification of molecular subtypes of heterogeneity uses Various ML models which propose different solutions and ML approaches, SVM, RF, and ABC are applied using python
	KARTHIK B; HEMALATHA B
	2023-10-18
专利权人	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
申请日期	2023-10-18
专利号	IN202341071161-A
成果简介	NOVELTY - Efficient classification of ovarian cancer using convolutional neural network (CNN) and artificial bee colony optimization uses Various ML (Machine Learning) models which propose different solutions and ML approaches, SVM (Support Vector Machine), RF (Random Forest), and ABC (Artificial Bee Colony) are applied using python programming and examined on ovarian cancer 8-7-02 dataset (OC_8702). USE - Efficient classification of ovarian cancer using CNN and artificial bee colony optimization used for research in ovarian cancer is the identification of different molecular subtypes of heterogeneity. ADVANTAGE - The efficient classification of ovarian cancer using CNN and artificial bee colony optimization helpful range detecting of Ovarian Cancer with early detection, it is crucial to know about ovarian cyst heterogeneity for selecting various treatment responses and forecasting the clinical outcomes of the patients, support Vector Machine and Artificial Bee Colony renders better outcomes in context to Accuracy, Precision, and Recall and to investigate the data and it becomes mandatory to choose the most significant genes or attributes for the whole data to prevent computational complexity.
IPC 分类号	A61K-039/39 ; A61P-035/00 ; G06K-009/62 ; G06N-020/00 ; G06N-003/00
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/19504
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	KARTHIK B,HEMALATHA B. Efficient classification of ovarian cancer using CNN and artificial bee colony optimization for research in ovarian cancer identification of molecular subtypes of heterogeneity uses Various ML models which propose different solutions and ML approaches, SVM, RF, and ABC are applied using python. IN202341071161-A[P]. 2023.

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