BUILDINGS FIRES FREQUENCY DETERMINING METHODOLOGY BASED ON DENSITY ESTIMATION AND SIMULATED ANNEALING METHODS

method In this paper, we propose a novel method for the automated assessment of knee osteoarthritisseverity, utilizing advanced machine learning techniques, specifically a deep neural network. Osteoarthritisis one of the most prevalent degenerative joint diseases, and its timely diagnosis is crucial forensuring effective treatment. Traditional methods for visually assessing X-ray images of the knee jointpresent several limitations, including subjectivity and reliance on the experience of the clinician. Therefore,the development of automated medical image analysis techniques has become increasingly relevant.Osteoarthritis of the knee joint is one of the most common and severe degenerative diseases leading to asignificant decrease in the quality of life of patients. Traditional methods of diagnosing osteoarthritis,such as visual assessment of X-ray images, depend on the subjective opinion of a specialist and his experience,which can lead to variations in the accuracy of diagnosis and timely detection of pathology. Therefore,the development and implementation of methods for automated analysis of medical images is highlyrelevant and has potential clinical value. In this study, we designed and trained a specialized neural networkbased on the ResNet-34 architecture, which has demonstrated significant effectiveness in solvingcomputer vision problems. The network was modified to incorporate two parallel branches, each containing a spiral linear structure and four hidden layers. This design enables more precise identification of theknee joint area. Additionally, the architecture facilitates optimization of the loss function to account forvarying pathological characteristics, such as different degrees of joint degradation, and to address theissue of class imbalance—a common challenge in medical imaging datasets. To further enhance modelperformance, the neural network was trained on two distinct datasets stratified by gender (male and female).This approach improved overall image quality and reduced the impact of noise introduced by artifactsduring radiographic imaging. Moreover, we employed the ImagePixelSpacing technique during datapreparation to standardize image resolution at 256 × 256 pixels, allowing for more accurate processingof fine details and structures within the knee joint. The network training employed state-of-the-art optimizationtechniques, resulting in a high level of classification accuracy. To evaluate the effectiveness of theproposed model, the Kappa test was utilized, confirming the reliability of baseline determinations.The model achieved an average accuracy of 93.76%, as demonstrated by the multiclass T-test, indicatingits strong potential for clinical application. Additionally, the model’s area under the curve (AUC) scorewas 0.97, surpassing the results reported in previous studies in this domain. In conclusion, this researchcontributes significantly to the field of medical informatics and computer-based medical image analysis byoffering an innovative solution for the automated assessment of osteoarthritis. This method has the potentialto profoundly improve diagnostic accuracy and treatment outcomes in clinical settings. In addition,these results demonstrate the potential of the model as a reliable tool for automated assessment of thedegree of osteoarthritis, which can not only improve the accuracy of diagnosis, but also facilitate the workof medical specialists. Further research may include adapting the model to analyze other joints and integratingadditional functionality, such as predicting disease progression based on sequential scans.