Transformer-Enhanced Dilated Convolutional Networks for Early Detection of Neurological Disorders

In The fusion of Transformer networks with convolutional architecture's has been very fruitful in launching the push for the early detection of neurological disorders. The current study introduces the Transformer Enhanced Dilations (TEDCN) in which it processes clinical records and past medical history to detect patterns characteristic of neurological conditions. The proposed model stands on the ability of Transformers to capture the long-range dependencies of the data, together with dilated convolutions that reinforce the receptive field without losing the resolution. The TEDCN model is an outcome of a research approach, which is based on the use of the Transformer network and dilated convolutions for better receptive field coverage with maximum resolution. Furthermore, data porosity can be met through the three diverse types of information, i.e., clinical data, medical histories, and diagnostics. The TEDCN can solve the massive, complex datasets and as a result, it can detect the disease early with high accuracy. The accuracy of the model to detect the slightest of the relationships amongst the previous records of a patient and disorders becomes a valid way of starting intervention and therefore healthcare management turns out to be benefitted. In the meantime, the proposed method has proved to be effective in early determination of neurological disorders after a thorough examination of various clinical data, thus shortening the gap between the detection and the treatment.