BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
Introduction
Cancer is a collection of diseases in which cells in the body come together to form lumps called malignant tumor. These cells grow in an uncontrollable way, spread into surrounding tissues and crowd out the normal cells [1]. From past to present, cancer is one of the most important disease that threaten human health [2]. In the study conducted in 2018, it has been estimated that 18.1 million cancer cases will be added to the existing cancer cases in the world and approximately 9.6 million of these cancer cases will result in death [2], [3]. There are more than 100 different types of cancer in medical science [4]. Nowadays, breast cancer is one of the most common type of cancer that is frequently mentioned among women and there are many studies on breast cancer in the scientific sense [5], [6], [7]. Breast cancer is the most commonly diagnosed cancer among women in 140 of 184 countries worldwide [8]. It has been estimated that approximately 20% of breast cancers over the world arise from modifiable risk factors, including alcohol use, excess body weight, and physical inactivity [9]. Therefore, early and accurate diagnosis of breast cancer is extremely important.
In the biomedical field, the examination and diagnosis of the breast cancer histopathological images by field experts is a sensitive and labor-intensive process requiring time and high qualification. The diagnosis process can be supported by utilizing existing technological tools and softwares. Thus, the cost and diagnosis effort can be significantly reduced. For this purpose, numerous studies have been conducted based on computational approaches. Support vector machines (SVMs) equipped with a feature selection algorithm has been introduced to detect breast cancer. 99.51% classification success was achieved in the study [1]. An expert system based on the association rules (AR) and neural network (NN) have been suggested for breast cancer diagnosis. AR ensured a reduction in the number of features whereas NN was employed for the intelligent classification. The accuracy of the expert system was 95.6% [10]. The fuzzy systems and evolutionary algorithms have been combined for the same purpose. The model provided a few simple rules for the experts’ interpretation [11]. Epithelial (EP) and stromal (ST) that are two types of tissues in histological images have been segmented automatically using the deep convolutional neural network (DCNN) and the model provided satisfactory results. The breast cancer histopathological images have been classified using ImageNet pre-trained DCNN model named as AlexNet. Various experiments were conducted on a public dataset. As a result, the model yielded 90% accuracy with deep fusion rules [12]. A novel breast cancer algorithm, convolutional neural network improvement for breast cancer classification (CNNI-BCC) has been suggested for the diagnosis task. The model was performed on the mammographic breast cancer medical images and reached 90.50% accuracy [13]. CNNI-BCC model has been offered to support medical experts in breast cancer diagnosis in a timely manner. CNNI-BCC model has a capable of classifying the incoming breast cancer medical images according to malignant, benign, and healthy. The model achieved 90.50% classification accuracy [13]. An intelligent diagnosis approach for breast cancer diagnosis has been suggested, which utilize information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection to reveal the top optimal feature performed the cost-sensitive support vector machine (CSSVM) learning algorithm. The model achieved 95.80% classification success [14]. In another study, incremental boosting convolutional networks have been introduced for providing an efficient diagnosis model of breast cancer from histopathological microscopic images. The model yielded an accuracy of 96.4% and 99.5% for four and two classes classification tasks, respectively [15].
It is clearly seen that numerous models have proposed to improve the efficiency of breast cancer diagnosis process. In this regard, one of the common models preferred for the diagnosis of breast cancer is the deep learning approach [16], [17], [18]. In this study, a novel deep learning model is proposed for breast cancer diagnosis. In the model that we call BreastNet, attention modules and hypercolumn technique were used together. In addition, more efficient qualifications have been obtained by applying the augmentation technique to the input images. The input data was set to 224 224 pixels. The general architecture of BreastNet consists of convolutional, dense and residual blocks. The CNN models such as AlexNet, VGG-16, VGG-19 were also used to compare the performance of the BreastNet model. The features obtained from the last fully connected layer (FC8) of the CNN models were provided as input to the Softmax activation function. Moreover, the classification performances of the models are examined separately. The experiments are carried out an open-access BreakHis dataset composed of histopathological images [12].
The rest of this study is organized as follows: In Section 2, the used publicly available dataset is described. In Section 3, the proposed novel CNN model, pre-trained deep CNN models, machine-learning algorithms and the steps of the experiments are presented. The experimental results are reported in Section 4. The discussion is given in Section 5. Lastly, concluding remarks are presented in Section 6.
Section snippets
Description of the BreakHis dataset
BreakHis is a dataset that includes totally 7909 images and eight sub-classes of breast cancers. The source data comes from 82 anonymous patients of Pathological Anatomy and Cytopathology Lab in Brazil. A normal and cancerous sample recording belongs to the dataset is shown in Fig. 1. The BreakHis dataset is divided into two main groups: benign tumors and malignant tumors. Of these patients, 24 have benign breast cancer and 58 have malignant breast cancer [12]. Histologically benign is a term
Deep learning models
In this section, the AlexNet [21], VGG-16, and VGG-19 [22] models are considered and described briefly. These three models have already announced their name in ImageNet competitions. AlexNet is one of the most important CNN architectures. This architecture consists of convolution, pooling and FC layers. The input size of this model is 227 227 pixels. Filters form the output of the related layer by applying convolution to the inputs got from the previous layer. Filters that are hovered over
Results
This study was carried out by using MATLAB (R2018b) and Python software. In all experiments, the present models were compiled using the MATLAB whereas the results of the proposed BreastNet model were obtained using the Python. Also, the BreakHis dataset was divided into two sets as 80% training and 20% testing, respectively. The present CNN models used in this study were used with the transfer learning approach. The parameter values of the present CNN models used in the experiments are given in
Discussion
Breast cancer is on the front bench type among hundreds of cancer diseases. The incidence of this disease is increasing day by day, especially among women. If this disease is not diagnosed in a timely manner, the rate of death is fairly high. The early diagnosis of this disease is associated with rapid and accurate results of the image processing techniques as regarding the computation approaches. In this scope, the CNN models have a great advantage in terms of giving faster and better results
Conclusion
The current study focused on improving the classification accuracy on the BreakHis data. It was comprised of the histopathological images separated into two different classes as benign or malignant. The classification accuracies of the BreastNet model is superior or approximate to the previously attempted techniques on the same dataset. This model can be used in all microscopic images at different magnification rates. The classification was carried out without using any preprocessing procedure
Funding
There is no funding source for this article.
Ethical approval
This article does not contain any data, or other information from studies or experimentation, with the involvement of human or animal subjects.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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