Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. In this study, we propose a chest X-ray image classification method based on spectral coefficients. The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. The procedure is applied to a latest data set of X-Ray images. The data set is composed of X-Ray images of three different classes of patients, normal, Covid infected and pneumonia. The moments designed in this study, when used in SVM or random forest improves its ability to classify a given X-Ray image at a high accuracy. A parametric study of the proposed approach is presented. The performance of these spectral moments is checked in Support vector machine and Random forest algorithm. The efficiency and accuracy of the proposed method is presented in details. All our simulation is performed in computation softwares, Matlab and Python. The image pre processing and spectral moments generation is performed in Matlab and the implementation of the classifiers is performed with python. It is observed that the proposed approach works well and provides satisfactory results (0.975 accuracy), however further studies are required to establish a more accurate and fast version of this approach.

Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset of visual fatigue during prolonged image analysis often compromise diagnostic accuracy, highlighting the need for automated diagnostic solutions. In this study, we present an advanced deep learning model, OA-HybridCNN (OHC), which integrates ResNet and DenseNet architectures. This integration effectively addresses the gradient vanishing issue in DenseNet and augments prediction accuracy. To evaluate its performance, we conducted a thorough comparison with other deep learning models using five-fold cross-validation and external tests. The OHC model outperformed its counterparts across all performance metrics. In external testing, OHC exhibited an accuracy of 91.77%, precision of 92.34%, and recall of 91.36%. During the five-fold cross-validation, its average AUC and ACC were 86.34% and 87.42%, respectively. Deep learning, particularly exemplified by the OHC model, has greatly improved the efficiency and accuracy of KOA imaging diagnosis. The adoption of such technologies not only alleviates the burden on radiologists but also significantly enhances diagnostic precision.

The use of artificial intelligence (AI) in medicine has shown promise to improve the quality of healthcare decisions. However, AI can be biased in a manner that produces unfair predictions for certain demographic subgroups. In MIMIC-CXR, a publicly available dataset of over 300,000 chest X-ray images, diagnostic AI has been shown to have a higher false negative rate for racial minorities. We evaluated the capacity of synthetic data augmentation, oversampling, and demographic-based corrections to enhance the fairness of AI predictions. We show that adjusting unfair predictions for demographic attributes, such as race, is ineffective at improving fairness or predictive performance. However, using oversampling and synthetic data augmentation to modify disease prevalence reduced such disparities by 74.7% and 10.6%, respectively. Moreover, such fairness gains were accomplished without reduction in performance (95% CI AUC: [0.816, 0.820] versus [0.810, 0.819] versus [0.817, 0.821] for baseline, oversampling, and augmentation, respectively).