IMPROVED DEEP LEARNING BASED MEDICAL IMAGE PROCESSING ON OBJECT DETECTION

This article seeks to introduce deep learning with Flower Pollination Algorithm (DFPA) from scientific foundations into applications in the analysis of medical images. Next, we begin examining the core principles and some frequently overlooked basic theory of the perceptive and neural networks. This helps one to understand why deep learning has increased in many areas of use. Obviously, the processing of medical images is one of the fields most influenced by this exponential advancement, in particular the identification and detection of images, image segmentation, imagery registration and computer assisted diagnostics. In simulation, modelling and regeneration, there are also recent developments which have shown stunning results. Some of these methods, however, lack previous experience and therefore face unpredictable outcomes.