Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Abstract:

Breast cancer is one of the major public health problem for women throughout the world. It has two states, known as benign and malignant. Benign state is slow growing, rarely spread to other parts of body and have well-defined borders. On the other hand, Malignant state has tendency to grow faster and it is life threatening. So, classification of this two state is crucial for proper diagnosis of a breast cancer patient. In this paper, we have introduced a new pipeline for breast cancer cell detection and feature extraction using an open source image analysis software named CellProfiler. We proposed an algorithm based on fuzzy inference system for classification of the benign and malignant state. Comparison using well known performance parameters such as accuracy, sensitivity and specificity shows that our proposed approach performs better than the Artificial Neural Network (ANN) and Support Vector Machine (SVM) based classification. The sensitivity, specificity, and accuracy of the proposed method is 95.6%, 90.63%, and 94.26% respectively.
Date of Conference: 22-24 Sept. 2016
Date Added to IEEE Xplore: 09 March 2017
ISBN Information:
INSPEC Accession Number: 16726596
Publisher: IEEE
Conference Location: Dhaka, Bangladesh

What we provide:

Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

Deliverables:-  

  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work

an interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling

an interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling

an interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling

Abstract

This paper presents an interactive fuzzy satisfying method based on evolutionary programming technique for short-term multiobjective hydrothermal scheduling. The multiobjective problem is formulated considering two objectives: (i) cost and (ii) emission. Assuming that the decision maker (DM) has fuzzy goals for each of the objective functions, evolutionary programming technique based fuzzy satisfying method is applied for generating a corresponding optimal noninferior solution for the DM’s goals. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. Thermal plants with nonsmooth fuel cost and emission level function are also taken into consideration. Results of the application of the proposed method are presented.

Publisher: ELSEVIER


What we provide:

Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

Deliverables:-  

  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work