Chest X-Ray Image Analysis with ResNet50, SMOTE and SafeSMOTE

Anh, Dao Nam; Dang, Xuan Tho

doi:10.1007/978-3-031-08580-2_18

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SMOTE is an oversampling approach that produces synthetic data using the nearest neighbor principle. Dao et al [27] introduced SMOTE in 2002, and it has been widely used as an oversampling technique. Oversampling, refers to the act of duplicating randomly selected data observations from the same class.…”

Section: Smotementioning

confidence: 99%

Artificial intelligence and feature based transfer learning framework for diagnosis of chest diseases using radiology images

Al-Otaibi,

Rehman,

Mujahid

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

The subject of artificial intelligence-assisted diagnosis and design in the medical industry is very exciting due to considerable developments in medical imaging. In real-world applications, previous manual feature extraction strategies were inefficient in achieving the required results. The number of medical image databases is quickly increasing to accommodate hospital-based diseases as a result of the numerous uses of medical images in healthcare facilities, pathology, and medical diagnostic fields. The primary objective of this study is to create a computerized Artificial intelligence system that can accurately diagnose different diseases and reduce mistakes in the testing process. The study has two primary aspects. In the initial phase, we utilized the deep transfer learning method to extract the pertinent and crucial features from the image X-ray. Subsequently, the support vector machine employs these crucial extracted features to diagnose diseases from the X-ray14 dataset. The imbalanced dataset problem was also addressed with the utilization of the Synthetic Minority Oversampling Technique (SMOTE). The authors conduct a comparative analysis of the findings from this study in relation to other cutting-edge studies and employ cross-dataset experiments to evaluate its efficacy. The results demonstrate that the proposed approach has a detection accuracy of 95.2\% for the disease. The VGG-16 model achieved 78.4\% accuracy and an AUC of 90\%. The proposed model can be applied to other diseases for further experiments.

show abstract

Section: Smotementioning

confidence: 99%