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

•Handcrafted features techniques for extracting texture, shape, and color features for efficient classification.•DNN classifier based on dense layer and softmax for efficient multi-classification.•Data augmentation method to address the problem of over fitting while improving classification accuracy is presented and segmentation on pre-processing stage.•Comparison of the proposed method and other works using histopathological images using BreakHis data.•The obtained results showcased the effectiveness of the proposed model for BC multi-classification. 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 an