ACO Inspired Computer-aided Detection/Diagnosis (CADe/CADx) Model for Medical Data Classification

Author(s): Anuradha Dhull*, Kavita Khanna, Akansha Singh, Gaurav Gupta

Journal Name: Recent Patents on Computer Science
Continued as Recent Advances in Computer Science and Communications

Volume 12 , Issue 4 , 2019

Graphical Abstract:


Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system.

Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature that keeping a balance between these performance parameters is a real challenge.

Methods: In order to achieve the desired objective, the following two contributions have been proposed to improve diagnosis accuracy: 1) an unsupervised feature selection approach based on ACO Meta-heuristic is used to design the CADe system, and 2) an ACO assisted decision tree classifier technique is employed to make CADx system.

Results: Three popular UCI (Wisconsin Breast Cancer, Pima Indian Diabetes and Liver Disorder) medical domain datasets have been used to evaluate the performance of the proposed model. The exploratory result analysis shows the efficiency of the proposed work as compared to existing work.

Keywords: Feature selection and ranking, fuzzy decision tree classifier, Distinct Class Split Measure (DCSM), Ant Colony Optimization (ACO), Computer Aided Detection (CADe), Computer Aided Diagnosis (CADx).

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Article Details

Year: 2019
Published on: 18 August, 2019
Page: [250 - 259]
Pages: 10
DOI: 10.2174/2213275912666181205155018
Price: $58

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