TY - GEN
T1 - AI-Enhanced Diagnosis
T2 - 7th International Conference on Image Information Processing, ICIIP 2023
AU - Gehlot, Naveen
AU - Soni, Khushi
AU - Kothari, Priyansh
AU - Vijayvargiya, Ankit
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The pediatric diseases in question are bronchiolitis and pneumonia, which pose a significant threat to children, especially those under ten years of age. Rapid diagnosis often requires a chest X-ray; reading and interpreting these images is challenging, and requires the expertise of a skilled doctor. It is essential to take advantage of advanced image recognition techniques to aid in interpreting these examinations and extracting necessary information. This study employed deep transfer learning models, including VGG16, VGG19, MobileNetV2, and InceptionResNetV2, to diagnose bronchiolitis and pneumonia in pediatric chest X-rays (PCXr) for the first time. Our findings show that the InceptionResNetV2 model has achieved the highest recall rate for bronchiolitis, with an impressive value of 78.82%. Following that, VGG16 achieved a recall rate of 77.64%, MobileNetV2 at 74.11%, and VGG19 at 62.35%. Furthermore, when assessing the models comprehensively based on their performance in terms of the F-score, InceptionResNetV2 outperformed the others with an F-score of 65.68%.
AB - The pediatric diseases in question are bronchiolitis and pneumonia, which pose a significant threat to children, especially those under ten years of age. Rapid diagnosis often requires a chest X-ray; reading and interpreting these images is challenging, and requires the expertise of a skilled doctor. It is essential to take advantage of advanced image recognition techniques to aid in interpreting these examinations and extracting necessary information. This study employed deep transfer learning models, including VGG16, VGG19, MobileNetV2, and InceptionResNetV2, to diagnose bronchiolitis and pneumonia in pediatric chest X-rays (PCXr) for the first time. Our findings show that the InceptionResNetV2 model has achieved the highest recall rate for bronchiolitis, with an impressive value of 78.82%. Following that, VGG16 achieved a recall rate of 77.64%, MobileNetV2 at 74.11%, and VGG19 at 62.35%. Furthermore, when assessing the models comprehensively based on their performance in terms of the F-score, InceptionResNetV2 outperformed the others with an F-score of 65.68%.
KW - Bronchiolitis
KW - Convolutional Neural Network (CNN)
KW - Pediatric Chest X-ray (PCXr)
KW - Pneumonia
UR - http://www.scopus.com/inward/record.url?scp=85195239256&partnerID=8YFLogxK
U2 - 10.1109/ICIIP61524.2023.10537715
DO - 10.1109/ICIIP61524.2023.10537715
M3 - Conference contribution
AN - SCOPUS:85195239256
T3 - Proceedings of the IEEE International Conference Image Information Processing
SP - 753
EP - 758
BT - 2023 7th International Conference on Image Information Processing, ICIIP 2023
A2 - Verma, Ruchi
A2 - Sharma, Vipul
A2 - Dhiman, Pankaj
A2 - Sehgal, Vivek Kumar
A2 - Gupta, Pradeep Kumar
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 November 2023 through 24 November 2023
ER -