Deep learning neural network (DLNN)-based classification and optimization algorithm for organ inflammation disease diagnosis

Abstract

Diagnostic image volume and complexity in healthcare system increases in rapid pace where available human pro ciency may not su cient for interpreting this much capacity of image data. Machine learning approaches exposed excessive potential to knob huge amount of two-dimensional annotated images of common illnesses from large databases. Deep learning imitates human for extracting knowledge from dataset and favourable to data scientists for accumulating, analysing, interpreting and predictive modelling. In this paper organ in ammation disease is addressed with Deep Learning Neural Network (DLNN) based classi cation scheme is incorporated to diagnose or prognoses the patient from severity, based on their historical database. In pandemic environment collecting histopathology tissue score is time consuming process due to a smaller number of physician availability, by implementing proposed DLNN algorithm suits for collecting organ in ammation score and categorizing its brutality by classi cation of pancreatitis, duodenum and appendix. In order to achieve accuracy and sensitivity of various stages soreness DLNN based algorithm is developed and it supports by classifying the datasets.