A hybrid deep neural network‐based automated diagnosis system using x‐ray images and clinical findings

Abstract

AbstractImage‐based computer‐aided diagnosis systems are frequently utilized to detect vital disorders. These systems consist of methods based on machine learning and work on data obtained from imaging technologies such as x‐rays, magnetic resonance imaging, and computed tomography. In addition to image data, clinical findings usually consist of text data that have a critical role in diagnosing diseases. In this study, an effective classification approach that can automatically detect diseases using a deep learning algorithm is proposed. A clinically usable deep machine‐learning model is presented with an approach based on the hybrid use of image processing and text processing methods. Although image‐processing techniques are used frequently in the literature by using images, text processing techniques are not often used in this field. The proposed method was evaluated on a dataset consisting of x‐ray images of subjects with and without Covid‐19 disease and their clinical notes. When the dataset was classified using image processing and text processing techniques accuracy values were obtained as 90.9% and 92.0% respectively. With the hybrid approach proposed in this study, the classification performance was increased and an accuracy value of 93.5% was obtained.