Disease Classification Using Hybrid Random Forest and Autoencoder on Image-Based Radiology Data

Abstract

Radiological images, including X-rays, MRI, and CT scans, are essential modalities for disease diagnosis. However, the complexity of medical image data presents significant challenges in developing accurate and reliable classification models within the field of radiology. This study proposes a hybrid model that integrates an Autoencoder with a Random Forest classifier for disease classification based on radiological images. The Autoencoder is utilized to extract deep feature representations from the images, while the Random Forest serves as a robust classification algorithm. The research workflow involves preprocessing radiological image data to ensure quality and consistency, developing and training the Autoencoder to generate meaningful feature representations, and implementing the Random Forest as the classification model using the features extracted by the Autoencoder. The performance of the proposed hybrid model is evaluated by comparing it with baseline approaches, such as convolutional neural networks (CNNs) and standalone Random Forest models, using evaluation metrics including accuracy, sensitivity, and specificity, which are critical for assessing classification performance in medical imaging. This research aims to advance artificial intelligence applications in medical imaging, specifically for supporting disease diagnosis. The proposed model is anticipated to provide an efficient and reliable solution for processing radiological image data, thereby enhancing diagnostic capabilities and supporting clinical decision-making in the healthcare sector. The integration of deep feature extraction with robust classification is expected to address existing challenges in medical image analysis and contribute to the development of more effective diagnostic tools.