Background:
One of the most prevalent sicknesses these days is breast cancer which is common amongst women. This sickness has been in increase to an alarming rate due to the lack of accurate administration of diagnoses. Early and accurate detection is one of the safest ways to cure a breast cancer patient.
Objectives:
The objective of this study is to proffer a more effective way to accurately classify a cancer sample; whether is Benign or Malignant.
Methods:
The classification model is based on the data collected from the UCI machine learning repository acquired from Wisconsin hospital called Wisconsin breast cancer data (WBCD). In this study, we preprocessed the dataset using DWT and then test the efficiency of deep learning (DL) for breast cancer classification. The model is developed using a feed-forward neural network and the result is compared with the observed values.
Results:
The result of the experiment proved the effectiveness of the proposed classification technique. The new technique accomplishes 98.90% accuracy for classifying breast cancer.
Conclusions:
The result from the experiment shows that the importance of data preprocessing and the efficiency of the neural network over other classification algorithms.
AN IMPROVED TRANSFER LEARNING APPROACH TOWARDS BREAST CANCER CLASSIFICATION ON DEEP RESIDUAL NETWORK
