Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

Abstract

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a twofold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cuttingedge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images. INDEX TERMS Breast cancer, histopathology medical images, deep learning, transfer learning, data augmentation, pre-processing, classification.

Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model

“…The scaled images were created by applying scaling factors of 0.5, 0.8, and 1.2 to each image. Using the horizontal and vertical reflections generates flipped images and the angles 40, 80, 120, and 180 degrees to rotate the image (Shahidi et al, 2020). The gamma correction used in the augmentation of the pictures shown in Figure 7 (a-f) ranges from 0.3 to 1.2.…”

Feature Fusion Based on Convolutional Neural Network for Breast Cancer Auxiliary Diagnosis

“…Deep learning requires a large amount of data as the support for training the network, but it is difficult to prepare enough data in practice, and due to the imbalance of the amount of data, the network will become too strict, causing the network model to overfit. In order to solve the problem of overlearning of malignant pathological images, data enhancement is needed [16,17]. Randomly crop the pathological image into the same pixel by random cropping, which can enhance the data image hundreds of times.…”

Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

“…Convolutional neural network (CNN) is a widely used deep learning (DL) algorithm in medical image-based classification and prediction. 7 Several methods use CNN in cancer detection and diagnosis 8 such as the Gleason grading of prostate cancer, [9][10][11] colon cancer grading, 12 breast cancer detection, 13,14 and pancreatic cancer detection [15][16][17][18] and classification. 19 However, grading of pancreatic cancer with DL still needs comprehensive study.…”

“…Overall, these results show that data augmentation may reduce overfitting and improve model performance as reported in. 10,13,14 Comparison analysis of model performance The overall performance results of all 14 different transfer-learning models proposed for this experiment are presented. Each model was trained with the 3 datasets and 5-fold cross-validation.…”