SLGP Header

Cognitive Image Processing for Determination of Skin Disease using Raspberry Pi3

IJEECC Front Page

In ancient and older times, Cognitive Science proves to be most vital solutions for the various problems in terms of the medical science. Our Human Body has peculiar feature that every problem inside body will be reflected as the diseases in the skins .But the recognition of that skin diseases is really nightmare. Hence we proposed the new technique called CIP (Cognitive Image Processing) for the reorganization of Image Segmentation. This biomedical Image processing is integrated in SOC using new algorithm called BIEE for the Power, Area and Rich Performance Characteristics. In recent times, usage of SOC is becoming so popular in the modern technology. These cores have been utilized for the different diagnosis systems which will be useful to the common man. As the applications and the number of cores increases, the energy and life time of the devices remains to be dark light. To design the High Performance and Energy Efficient Algorithms for Bio Medical Image Processing Systems for pre-detection of the skin diseases with the High end Architectures using Raspberry Pi 3.
Keywords:Cognitive Image Processing (CIP), Bio-Intelligent Energy Efficiency (BIEE), System on Chip (SOC), Field Programmable Gate Array (FPGA), Camera Serial Interface (CSI), Bluetooth Classic & Low Energy (BLE).


  1. Skin color segmentation for detecting human face region in image Prashanth Kumar G and Shashidhara M.
  2. Nikhil, R. P., Sankar, K. P., A review on image segmentation techniques, Pattern Recognition, Volume 26, Issue 9, September 1993, Pages 1277-1294, ISSN 0031-3203.
  3. Bhavini J. Samajpati ,M.E Student of Computer Department, Sheshang D. Degadwala, Assistant Professor, ―A Survey on Apple Fruit Diseases Detection and Classification‖, International Journal of Computer Applications (0975 – 8887) Volume 130 – No.13, November 2015.
  4. Adaptive Automatic Segmentation of HEp-2 Cells in Indirect Immunofluorescence Images Yu-Len Huang; Yu-Lang Jao; Tsu-Yi Hsieh; Chia-Wei Chung.