Enhancing Biomedical Image Interpretation Through a Hybrid Machine Learning Algorithm

In contemporary medicine, biomedical image interpretation is essential for disease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to erroneous conclusions. The proposed work offers a novel approach to resolving these issues by utilizing a mixed machine-learning technique to improve the interpretation of biological images. The proposed system accurately evaluates physical images using machine learning techniques, including convolutional neural networks and decision trees. The algorithm aims to integrate the most beneficial aspects of multiple image analysis methods to enhance their overall performance. Using a carefully selected dataset, demonstrate the algorithm's precision and robustness compared to other approaches. The findings imply that the algorithm could substantially alter the interpretation of biological images in clinical and academic settings. This discovery has far-reaching implications, paving the way for improved diagnostic precision and further study of the human body. This hybrid method is a promising step toward automating image processing and paves the way for new research and implementation opportunities in healthcare technology.