Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer)

Abstract

Breast cancer (BC) classification has become a point of concern within the field of biomedical informatics in the health care sector in recent years. This is because it is the second-largest cause of cancer-related fatalities among women. The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Proper detection and monitoring contribute immensely to the survival of BC patients, which is largely dependent on the analysis of pathological images. Automatic detection of BC based on pathological images and the use of a Computer-Aided Diagnosis (CAD) system allow doctors to make a more reliable decision. Recently, Deep Learning algorithms like Convolution Neural Network have been proven to be reliable in detecting BC targets from pathological images. Several research efforts have been undertaken in the binary classification of histopathological images. However, few approaches have been proposed for the multi-classification of histopathological images. The classification accuracy produced by these approaches are inefficient since they considered only texture-based extracted features and they used some techniques that cannot extract some of the main features from the images. Also, these techniques still suffered from the issue of overfitting. In this work, handcrafted feature extraction techniques (Hu moment, Haralick textures, and color histogram) and Deep Neural Network (DNN) are employed for breast cancer multi-classification using histopathological images on the BreakHis dataset. The features extracted using the handcrafted techniques are used to train the DNN classifiers with four dense layers and Softmax. Further, the data augmentation method was employed to address the issue of overfitting. The results obtained reveal that the use of handcrafted approach as feature extractors and DNN classifiers had a better performance in breast cancer multi-classification than other approaches in the literature. Moreover, it was also noted that augmentation of data plays a key role in further improvement of classification accuracy. The proposed method achieved an accuracy score of 97.87% for 40x, 97.60% for 100x, 96.10% for 200x, and 96.84% for 400x for the magnification-dependent histopathological images classification. The results also showed that the proposed method for using the Handcrafted feature extraction method with DNN classifier had a better performance in multi-classification of breast cancer using histopathological images than most of the related works in the literature.