Development of a Framework for Preserving the Disease-Evidence-Information to Support Efficient Disease Diagnosis

In medical domain, the detection of the acute diseases based on the medical data plays a vital role in identifying the nature, cause, and the severity of the disease with suitable accuracy; this information supports the doctor during the decision making and treatment planning procedures. The research aims to develop a framework for preserving the disease-evidence-information (DEvI) to support the automated disease detection process. Various phases of DEvI include (1) data collection, (2) data pre- and post-processing, (3) disease information mining, and (4) implementation of a deep-neural-network (DNN) architecture to detect the disease. To demonstrate the proposed framework, assessment of lung nodule (LN) is presented, and the attained result confirms that this framework helps to attain better segmentation as well as classification result. This technique is clinically significant and helps to reduce the diagnostic burden of the doctor during the malignant LN detection.