COMBINATION OF DEEP LEARNING AND MACHINE LEARNING FOR AI-ASSISTED DIAGNOSTICS

Abstract

This work proposes a hybrid system that combines ResNet (Residual Network - widely recognized for its impact on deep learning, being a milestone in the area of computer vision) with Extremely Random Trees (Extra Trees) to classify chest X-ray images and assist in the detection of diseases. The approach uses the Transfer Learning technique, where ResNet, previously trained, is used to extract relevant characteristics from the images. Then, the Extra Trees algorithm performs the classification based on these characteristics. In the initial stage, using only ResNet combined with a small neural network, we obtained an accuracy of 95.40% in validation and 79.33% in tests. With the implementation of the hybrid system, the results were significantly improved, reaching 96.90% accuracy in validation and 89.98% in tests, representing a significant improvement of approximately 10 percentage points in tests. These results highlight the potential of the hybrid system in applications, demonstrating how the combination of advanced deep learning and machine learning techniques can contribute significantly to improving accuracy.

DOI: https://doi.org/10.56238/sevened2025.008-005