Utilizing Chest X-rays for Age Prediction and Gender Classification

Christodoulos Solomou; Dimitar Lubomirov Kazakov

Utilizing Chest X-rays for Age Prediction and Gender Classification

Christodoulos Solomou, Dimitar Lubomirov Kazakov

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we present a framework for automatically predicting the gender and age of a patient using chest x-rays (CXRs). The work of this paper derives from common situations in medical imaging where the gender/age of a patient might be missing or in situations where the x-ray is of poor quality, thus leaving the medical practitioner unable to treat the patient appropriately. The proposed framework comprises of training a large CNN which jointly outputs the gender/age of a CXR. For feature extraction, transfer learning was employed using the EfficientNetB0 architecture, with a custom trainable top layer for both classification and prediction. This framework was applied to a combination of publicly available data, which collectively represent a heterogeneous dataset showing a variation in terms of race, location, patient's health, and quality of image. Our results are robust with respect to these factors, as none of them was used as input to improve the results. In conclusion, Deep Learning can be implemented in the medical imaging domain for automatically predicting characteristics of a patient.

Original language	English
Title of host publication	Proceedings of the 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781665401500
Publication status	Published - 16 Dec 2021
Event	INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS - Virtual conference, YOGYAKARTA, Indonesia Duration: 16 Dec 2021 → 17 Dec 2021 Conference number: 4 https://isriti.akakom.ac.id/index.html

Conference

Conference	INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS
Abbreviated title	ISRITI
Country/Territory	Indonesia
City	YOGYAKARTA
Period	16/12/21 → 17/12/21
Internet address	https://isriti.akakom.ac.id/index.html

Bibliographical note

© IEEE, 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Keywords

x-rays

Access to Document

UtilizingChestX-raysforAgePredictionandGenderClassification
© IEEE, 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details
Accepted author manuscript, 512 KBLicence: Unspecified

Cite this

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title = "Utilizing Chest X-rays for Age Prediction and Gender Classification",

abstract = "In this paper we present a framework for automatically predicting the gender and age of a patient using chest x-rays (CXRs). The work of this paper derives from common situations in medical imaging where the gender/age of a patient might be missing or in situations where the x-ray is of poor quality, thus leaving the medical practitioner unable to treat the patient appropriately. The proposed framework comprises of training a large CNN which jointly outputs the gender/age of a CXR. For feature extraction, transfer learning was employed using the EfficientNetB0 architecture, with a custom trainable top layer for both classification and prediction. This framework was applied to a combination of publicly available data, which collectively represent a heterogeneous dataset showing a variation in terms of race, location, patient's health, and quality of image. Our results are robust with respect to these factors, as none of them was used as input to improve the results. In conclusion, Deep Learning can be implemented in the medical imaging domain for automatically predicting characteristics of a patient.",

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author = "Christodoulos Solomou and Kazakov, {Dimitar Lubomirov}",

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AU - Solomou, Christodoulos

AU - Kazakov, Dimitar Lubomirov

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N2 - In this paper we present a framework for automatically predicting the gender and age of a patient using chest x-rays (CXRs). The work of this paper derives from common situations in medical imaging where the gender/age of a patient might be missing or in situations where the x-ray is of poor quality, thus leaving the medical practitioner unable to treat the patient appropriately. The proposed framework comprises of training a large CNN which jointly outputs the gender/age of a CXR. For feature extraction, transfer learning was employed using the EfficientNetB0 architecture, with a custom trainable top layer for both classification and prediction. This framework was applied to a combination of publicly available data, which collectively represent a heterogeneous dataset showing a variation in terms of race, location, patient's health, and quality of image. Our results are robust with respect to these factors, as none of them was used as input to improve the results. In conclusion, Deep Learning can be implemented in the medical imaging domain for automatically predicting characteristics of a patient.

AB - In this paper we present a framework for automatically predicting the gender and age of a patient using chest x-rays (CXRs). The work of this paper derives from common situations in medical imaging where the gender/age of a patient might be missing or in situations where the x-ray is of poor quality, thus leaving the medical practitioner unable to treat the patient appropriately. The proposed framework comprises of training a large CNN which jointly outputs the gender/age of a CXR. For feature extraction, transfer learning was employed using the EfficientNetB0 architecture, with a custom trainable top layer for both classification and prediction. This framework was applied to a combination of publicly available data, which collectively represent a heterogeneous dataset showing a variation in terms of race, location, patient's health, and quality of image. Our results are robust with respect to these factors, as none of them was used as input to improve the results. In conclusion, Deep Learning can be implemented in the medical imaging domain for automatically predicting characteristics of a patient.

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T2 - INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS

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