A Hybridized Early Detection, Classification and Diagnosis of Breast Cancer Using Deep Learning Algorithm

  • Mohammed Abubakar Department of Computer Science, Mai Idris Alooma Polytechnic, Geidam, Yobe State
  • Ganga Mohammed Kaana Registry Department, Mai Idris Alooma Polytechnic Geidam Yobe State, Nigeria
  • Musa Wakil Bara Department of Computer Science, Mai Idris Alooma Polytechnic Geidam, Yobe State, Nigeria
  • Ibrahim Sani Department of Statistics, Mai Idris Alooma Polytechnic Geidam, Yobe State
Keywords: Breast cancer, machine learning, deep learning


There is a rise in the cases of breast cancer in low income population like northern Nigeria. Breast cancer is considered as one the major killer diseases among women of child bearing age (WCBA) (Aslan et al., 2018). There have been many researches on the identification and diagnosis of breast cancer disease for decades however, some of these researches are manual based and are inefficient due to their time consumption. With the recent advancement in ICT, machine learning algorithms are used to classify images. A great achievement was made in classifying and detecting breast cancer using traditional machine learning algorithms such as Decision Tree and Artificial Neural Network (ANN).

Despite the performance of these traditional machine learning algorithms in cancer prediction and diagnosis, there are common limitations that need to be addressed. These limitations include manual feature selection, fewer number of classes in classifying tumour (usually being classified into two classes) and their inability to classify larger dataset on time. This research is aimed at improving the performance of traditional machine learning algorithms by using a Deep Learning algorithms. Deep learning algorithm gives a promising result in image classification and can therefore extend the number of classes to more than the usual two (2) and solve the problem of fewer classes.