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Journal of Medical Systems

, 42:157 | Cite as

Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images

  • Enas M. F. El Houby
Image & Signal Processing
  • 101 Downloads
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

Early detection of cancer can increase patients’ survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance Imaging, and microscopic images are the common method for cancer diagnosis. Recently, computer-aided diagnosis (CAD) systems have been used to help physicians in cancer diagnosis so that the diagnosis accuracy can be improved. CAD can help in decreasing missed cancer lesions due to physician fatigue, reducing the burden of workload and data overloading, and decreasing variability of inter- and intra-readers of images. In this research, a framework of CAD systems for cancer diagnosis based on medical images has been proposed. The proposed work helps physicians in detection of suspicion regions using different medical images modalities and in classifying the detected suspicious regions as normal or abnormal with the highest possible accuracy. The proposed framework of CAD system consists of four stages which are: preprocessing, segmentation of regions of interest, feature extraction and selection, and finally classification. In this research, the framework has been applied on blood smear images to diagnose the cases as normal or abnormal for Acute Lymphoblastic Leukemia (ALL) cases. Ant Colony Optimization (ACO) has been used to select the subsets of features from the features extracted from segmented cell parts which can maximize the classification performance as possible. Different classifiers which are Decision Tree (DT), K-nearest neighbor (K-NN), Naïve Bayes (NB), and Support Vector Machine (SVM) have been applied. The framework has been yielding promising results which reached 96.25% accuracy, 97.3% sensitivity, and 95.35% specificity using decision tree classifier.

Keywords

Acute lymphoblastic leukemia Classification Computer-aided diagnosis Segmentation Machine learning techniques 

Notes

Acknowledgments

This work was funded by National Research Centre (NRC), Cairo, Egypt. Authors are grateful to NRC for funding the project (grant number 11090333).

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interests to declare.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Systems & Information Department, Engineering Research Division, National Research CentreCairoEgypt

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