A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

Computer-aided diagnosis has emerged as one of the main areas of study for radiology diagnosis and medical imaging in recent years. Also, developing a single prediction methodology for handling multiple types of medical images is remains one of the most significant issues in recent times. For handling various kinds of medical images, this research presents Smart Multimodal Disease Detection (SMD₂), an innovative and powerful automated method. The proposed framework’s contribution is the ability to use various kinds of medical images to carry out an accurate and efficient disease diagnosis. The Woodpecker Mating Optimization Algorithm (WpMO) approach is used to optimally choose the most important features from the provided inputs, simplifying the classification process. In addition, the innovative Squeeze Excitation Capsule Network (SECNet) model is used to accurately identify and classify the disease class with a reduced computational time and complexity. A range of various medical imaging datasets, including X-ray, CT, and MRI, are considered for study in order to validate the performance outcomes of the proposed model. The results of the investigation indicate that the loss value of the proposed approach has dropped to 1.3, but its average accuracy has grown by 99%.