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Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features

Journal of Medical Systems volume 43, Article number: 332 (2019) Cite this article

Abstract

Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods.

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Acknowledgments

This work was supported by Research Project [Lung Cancer Diagnosis from CT Images]; Prince Sultan University; Saudi Arabia [SSP -18-5-03]”. Additionally, this work was partially supported by Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia.

Funding

This work was supported by Research and Innovation Center through grant number SSP-18-5-03; Prince Sultan University, Riyadh, Saudi Arabia. This work was also partially supported by AI & Data Analytics Lab (AIDA), Prince Sultan University, Riyadh, Saudi Arabia.

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Affiliations

  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Tanzila Saba, Ahmed Sameh & Fatima Khan

  2. Department of Computer Science, Luther College, Decorah, IA, USA

    Shafqat Ali Shad

  3. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Muhammad Sharif

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  1. Tanzila Saba
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  2. Ahmed Sameh
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  4. Shafqat Ali Shad
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Correspondence to Tanzila Saba or Muhammad Sharif.

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Saba, T., Sameh, A., Khan, F. et al. Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features. J Med Syst 43, 332 (2019). https://doi.org/10.1007/s10916-019-1455-6

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Keywords

  • Cells
  • Texture
  • Benign
  • VGG 19
  • SVM