Abstract
Breast cancer is the world’s top cancer affecting women. While the danger of the factors varies from a place, lifestyle, and diet. Treatment procedures after discovering a confirmed cancer case can reduce the risk of the disease. Unfortunately, breast cancers that arise in low and middle-income countries are diagnosed at a very late stage in which the chances of survival are impeded and reduced. Early detection is therefore required not only to improve the accuracy of discovering breast cancer but also to increase the chances of making the right decision on a successful treatment plan. There have been several studies tending to build software models utilizing machine learning and soft computing techniques for cancer detection. This research aims to build a model scheme to facilitate the detection of breast cancer and to provide the exact diagnosis. Improving the accuracy of a proposed model has, therefore, been one of the key fields of study. The model is based on deep learning that intends to develop a framework to accurately separate benign and malignant breast tumors. This study optimizes the learning algorithm by applying the Dragonfly algorithm to select the best features and perfect parameter values of the deep learning model. Moreover, it compares deep learning results against that of support vector machine (SVM), random forest (RF), and k nearest neighbor (KNN). Those classifiers are chosen as they are the most reliable algorithms having a solid fingerprint in the field of clinical data classification. Consequently, the hybrid model of deep learning combined with binary dragonfly has accurately classified between benign and malignant breast tumors with fewer features. Besides, deep learning model has achieved better accuracy in classifying Wisconsin Breast Cancer Database using all available features.
Highlights
Breast cancer is the most common cancer in women and, overall, the second most leading to death
Some experiments handled missing data by Mean Imputation technique and others by Missing Data Ignoring Technique. It declares that random forest (RF) gives a better result when using 10 trees, and k nearest neighbor (KNN) with 3 neighbors which reduces the complexity of the model and consumes less processing time
This study aims to enhance the accuracy of the diagnosis of breast cancer with the deep learning method
Summary
Breast cancer is the most common cancer in women and, overall, the second most leading to death. Many methods have been constantly developed to achieve accurate and efficient diagnosis results and several experiments were performed on the WBCD using multiple classifiers and feature selection techniques. Many of them show a good classification accuracy, for example, in [5] the performance criterion of supervised learning classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM-RBF) kernel, and neural networks (NN) are compared to find the best classifier using the dataset (WBCD), and the SVM-RBF has the best outcome achieving 96.84%. The accuracy obtained from the system which combines rough set theory with backpropagation neural network in [10] is 98.6% on the breast cancer dataset. The algorithm KNN for classification which is used in [11] with several different types of distances and classification rules is used in the diagnosis and classification of cancer, and these experiments are conducted on the database WBCD. This work aims to automatically design and modify the parameters of the deep learning model hybrid with the Dragonfly algorithm for breast cancer diagnosis
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