DIFDD: Deep intelligence framework for disease detection using patients electrocardiogram signals and X-ray images

Heart disease has been the leading cause of mortality worldwide in the recent decade. Since 2019, new lung-related infections have increased heart attack mortality. To minimize mortality, a unified framework is needed to predict early diagnosis utilizing different patient data. Existing methods failed to produce automatic solutions on the unified approach to cardiac problems considering lung infections. A unique automated disease diagnosis and classification framework using patient chest X-ray images and Electrocardiogram (ECG) signal is proposed. This integrated framework is unique in diagnosing and monitoring lung disease and cardiac problems utilizing patient X-ray and ECG. The proposed system is called the Deep Intelligence Framework for Diseases Detection (DIFDD). DIFDD procedures include pre-processing, automated feature extraction, and classification. An effective pre-processing method is designed to improve X-ray and ECG data. The 2D and 1D Convolutional Neural Network (CNN) techniques are proposed to extract automated features from pre-processed X-ray and ECG data. According to feature learning, automated detection uses several classifiers. Based on classifier results, a consolidated approach is presented for medical judgment on the patient's health for suitable treatment. The simulation results using the synthetic dataset revealed the efficiency of the proposed DIFDD model over the existing methods. The overall accuracy of the DIFDD model is improved by 1.5% and computational overhead is reduced by 13.56%.