DEEP LEARNING MODEL FOR DETECTING BREAST CANCER

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DEEP LEARNING MODEL FOR DETECTING BREAST CANCER AT EARLY STAGE

Abstract

Breast cancer is the most common cancer among women but in can occur in both the genders. It is accountable for an appalling number of deaths worldwide. In a particularly  developing country like Nigeria, there is a lack of awareness and facilities mostly in rural areas and high rate of instances of breast cancer that is diagnosed in the last stages. However, the early detection of breast cancer can lead to help increase the odds of survival. Nowadays, with the increasing number of patients, manual analysis of medical images becomes tedious, time consuming and unfeasible.  With  the advancement in the field of machine learning,   it is now possible to create an automated and accurate Computer Aided Diagnosis (CAD) system in order to make the entire process of detecting a malignant tumor more resource efficient and time saving through proper utilization. This paper presents the comparative analysis of different machine learning algorithms and their results in predicting cancerous tumors. The proposed model uses supervised machine learning algorithms such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Logistic Regression with and without PCA on a dataset with 30 features extracted from a digitized image of a fine needle aspirate (FNA) of a breast mass. Deep learning models like Artificial Neural Network and Convolutional Neural Network are used and their performances are compared. From the comparative analysis, it is observed that the deep learning models outperform all other classifiers and achieves impressive scores across multiple performance metrics such as Accuracy of 98.83%, Precision of 98.44% and Recall of 100%.

LISTED CONTENTS FOR DEEP LEARNING MODEL FOR DETECTING BREAST CANCER Pdf

  • CHAPTER ONE
  • Introduction
  • Motivation
  • Objective
  • Thesis Orientation
  • CHAPTER TWO
  • Literature Review
  • Related Works .
  • Machine Learning
  • proposed Model
  • Dataset
  • Data Visualization
  • Histogra
  • Heatmap
  • Data Preprocessing
  • Categorical Variable Conversion
  • Feature Scaling
  • Principal Component Analysis (PCA)
  • Data Reshaping
  • Train-Test Split
  • Neural Network Layers
  • Algorithms
  • Random Forest
  • Support Vector Machine
  • K-Nearest Neighbor
  • Logistic Regression
  • Naïve Bayes
  • Artificial Neural Network (ANN)
  • Convolutional Neural Network (CNN
  • CHAPTER THREE
  • Result Analysis
  • Performance Metrics
  • Confusion matrix
  • Accuracy
  • Precision
  • Recall or Sensitivity
  • F1 Score
  • Receiver Operating Characteristics (ROC) Curve
  • Area under the ROC Curve (AUC)
  • Model Performances
  • Random Forest (RF)
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Logistic Regression (LR)
  • Naïve Bayes
  • Artifical Neural Network (ANN)
  • Convolutional Neural Network (CNN)
  • CHAPTER FIVE
  • Discussion
  • Conclusion
  • References

Contents