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A Robust Machine learning based method to classify normal and abnormal CT scan images of mastoid air cells

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Abstract

The abnormality of mastoid air cells represents various types of ear maladies. The current traditional manual analyzing of huge amount of collected images from ear cavity is time consuming and the low accuracy of diagnosing these abnormalities by humans is inevitable, thus the subsequent consequences could threaten the patient's health. This study presents an automated machine learning based method to classify normal and abnormal mastoid air cells using CT images procured in the clinical center. Methods This paper introduces the first robust method based on convolutional layers and deep neural network to classify normal and abnormal mastoid air cells. The used dataset is comprised of total of 24,800 (right and left mastoid) CT slides of 152 patients who have been referred to the Tabriz Golgasht Imaging Center(TGIC) at the request of the ENT specialist which include the mastoid air cells from most upper to the lowest part of the ear cavity. Results The proposed fully automatic classification and diagnosing method provides a promising result compared to the manual classification by ENT specialists. In our classification algorithm the accuracy, f 1_score, Precision, Recall, were 98.10%, 98.05%, 98.32%, 97.89% respectively(over the five-fold cross-validation on validation dataset) and the accuracy of this method on test data was 97.56% (the average of 5 times running of five-fold cross-validation). The robustness and efficiency of the proposed method are demonstrated by comparison with some of most common deep learning architectures ResNet50 and AlexNet. Conclusions The proposed machine learning method directly learned from B-scan labels, requiring no manual detailed annotations at image. Medically, the image investigation of ear CT scan images mainly remains at the doctor’s manual diagnosis stage, but manual examination and diagnosis could be labor intensive and time consuming. In this paper, a deep convolutional neural network (ConvNet) is used to achieve automatic classification of mastoid air cells using CT images by analyzing the characteristics of the patient’s CT images of the ear cavity.

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Data availability

The data are unavailable for public access because of concerns about privacy of patients but are available from the corresponding author upon reasonable request approved by Faculty of Advanced Medical Sciences.

Code availability

'Not applicable'.

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Acknowledgements

The authors would like to thank the Medical Bioengineering Department, School of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz, Iran.

Funding

This work is partially supported by vice-chancellery for research and technology of Tabriz University of Medical Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Student Research Committee, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Mohammad Khosravi

  2. Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Mohammad Khosravi & Mahdad Esmaeili

  3. Department of Otolaryngology, Head and Neck Surgery, Tabriz University of Medical Sciences, Tabriz, Iran

    Yalda Jabbari Moghaddam

  4. Medical Physics Department, Medical Faculty, Tabriz University of Medical Sciences, Tabriz, Iran

    Ahmad Keshtkar

  5. Department of Radiology, Faculty of Medicine, Imam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran

    Javad Jalili

  6. Department of Anatomical Sciences, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

    Hamid Tayefi Nasrabadi

Authors
  1. Mohammad Khosravi
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  2. Mahdad Esmaeili
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  3. Yalda Jabbari Moghaddam
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  4. Ahmad Keshtkar
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  5. Javad Jalili
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  6. Hamid Tayefi Nasrabadi
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Contributions

Dr. Yalda Jabbari Moghaddam, Dr. Ahmad Keshtkar and Dr. Mahdad Esmaeili conceived of the presented idea and designed the study. Dr. Mahdad Esmaeili,Mohammad Khosravi and Dr. Hamid Tayefi Nasrabadi carried out the experiments. Dr. Javad Jalili and Dr. Yalda Jabbari Moghaddam jointly performed the manual ground truth labeling. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Mahdad Esmaeili or Yalda Jabbari Moghaddam.

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This work is partially supported by vice-chancellery for research and technology of Tabriz University of Medical Sciences under the ethical code number IR.TBZMED.VCR.REC.1398.378.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Khosravi, M., Esmaeili, M., Moghaddam, Y.J. et al. A Robust Machine learning based method to classify normal and abnormal CT scan images of mastoid air cells. Health Technol. 12, 491–498 (2022). https://doi.org/10.1007/s12553-022-00653-y

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  • DOI: https://doi.org/10.1007/s12553-022-00653-y

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