• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Iqbal, Saeed (Iqbal, Saeed.) | Qureshi, Adnan N. (Qureshi, Adnan N..) | Li, Jianqiang (Li, Jianqiang.) (学者:李建强) | Choudhry, Imran Arshad (Choudhry, Imran Arshad.) | Mahmood, Tariq (Mahmood, Tariq.)

收录:

Scopus SCIE

摘要:

Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, with limited findings coming from significant instances of the novel illness. We offer a technique that allows a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. Deep learning techniques are used to train and evaluate images, enabling the extraction of basic visual attributes. The training objects' characteristics, instances, categories, and relative data modeling are all represented probabilistically. It is possible to identify a minority category in the classification process by using an imbalance-based sample analyzer. In order to address the imbalance problem, learning samples from the minority class are examined. The Support Vector Machine (SVM) is used to categorize images in clustering. Physicians and medical professionals can use the CNN model to validate their initial assessments of malignant and benign categorization. The proposed technique for class imbalance (3-Phase Dynamic Learning (3PDL)) and parallel CNN model (Hybrid Feature Fusion (HFF)) for multiple modalities achieve a high F1 score of 96.83 and precision is 96.87, its outstanding accuracy and generalization suggest that it may be utilized to create a pathologist's help tool.

关键词:

Class imbalance Random sampling Convolutional neural network Feature fusion Ensemble learning Dynamic learning

作者机构:

  • [ 1 ] [Iqbal, Saeed]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Jianqiang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Iqbal, Saeed]Univ Cent Punjab, Fac Informat Technol & Comp Sci, Dept Comp Sci, Lahore, Pakistan
  • [ 4 ] [Qureshi, Adnan N.]Univ Cent Punjab, Fac Informat Technol & Comp Sci, Dept Comp Sci, Lahore, Pakistan
  • [ 5 ] [Choudhry, Imran Arshad]Univ Cent Punjab, Fac Informat Technol & Comp Sci, Dept Comp Sci, Lahore, Pakistan
  • [ 6 ] [Li, Jianqiang]Beijing Engn Res Ctr IoT Software & Syst, Beijing 100124, Peoples R China
  • [ 7 ] [Mahmood, Tariq]Univ Educ, Fac Informat Sci, Vehari Campus, Vehari 61100, Pakistan
  • [ 8 ] [Mahmood, Tariq]Prince Sultan Univ, Coll Comp & Informat Sci CCIS, Artificial Intelligence & Data Analyt AIDA Lab, Riyadh 11586, Saudi Arabia

通讯作者信息:

查看成果更多字段

相关关键词:

相关文章:

来源 :

HELIYON

年份: 2023

期: 6

卷: 9

4 . 0 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 27

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:407/4912054
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司