Image Retrieval using Bag-of-Features for Lung Cancer Classification

被引：3
|
作者：
Bhatt, Shital D. [1 ]
Soni, Himanshu B. [2 ]
机构：
[1] Madhuben & Bhanubhai Patel Inst Technol, Dept Informat Technol, Anand, Gujarat, India
[2] GH Patel Coll Engn & Technol, Dept Elect & Commun, Anand, Gujarat, India
来源：
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021) | 2021年
关键词：
Bag of features (BOF); Content Based Image Retrieval (CBIR); K-Means clustering; Speeded-Up Robust Features (SURF); PULMONARY NODULE DETECTION; CT; MODEL;
D O I：
10.1109/ICICT50816.2021.9358499
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, Content Based Image Retrieval (CBIR) technique is used to retrieve the images from a large database that are identical to the image provided as a query. The CBIR based Bag-of-Features (BOF) technique is used for extracting features and their classification on the dataset of LIDC/IDRI Therefore, a lung image retrieval using grid-based feature extraction method and Speeded-Up Robust Features (SURF) algorithm are implemented in this paper. K-Means clustering algorithm is extended to the features extracted by SURF algorithm. MATLAB simulation is performed on selective database, which provides 99% and 98.56 % of training and testing accuracy respectively.
引用
收藏
页码：531 / 536
页数：6
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