Integrating Mental Healthcare and Medical Image Processing Using ResNet, and DenseNet

Abstract

The creation of a strong and effective data processing model catered for mental healthcare picture analysis is the main emphasis of this study. The technique starts with gathering pertinent picture data and then uses Huffman encoding to compress data to cut size. Advanced filtering methods help to remove noise from the compressed photos therefore guaranteeing high-quality data. The data is then divided in a 70:30 ratio into training and testing sets, therefore enabling a fair assessment of the performance of the model. The problem of over fitting is addressed using an Ensemble Model combining ResNet and DenseNet architectures, therefore guaranteeing that the model generalizes effectively to unseen data. Along with improved resistance against different cyber-attacks, the predicted results include shorter processing times, fewer error rates, and smaller packet sizes. The Proposed Ensemble Model achieved the highest Accuracy of 95.80%, Precision of 94.50%, Recall of 96.30%, F1-Score of 95.40% and minimum Time Consumption of 120s and Memory Usage of 350MB. Furthermore, the study seeks to enhance picture quality by means of noise filtering, thereby preserving the integrity of compressed data. With possible uses in real-time and resource-limited contexts, our study promises to improve the accuracy and security of mental healthcare picture analysis.