Classification of breast cancer images using new transfer learning techniques

Abstract

Breast cancer is one of the most common types of cancer among women, which requires building smart systems to help doctors and early detection of cancer. Deep learning applications have emerged in many fields, especially in health care, but there are still some limitations in this technique, such as the small number of classified medical images needed to train deep learning models, to solve this problem, transfer learning technology appeared based on transferring knowledge from pre-trained models on ImageNet and setting them to the current task, but there is still a problem that the features extracted from ImageNet are not medical, so this paper aims to find a solution to this problem by using new techniques for transfer learning by taking advantage of the presence of unclassified medical images of the same disease to reduce the impact of ImageNet. The proposed approach was applied to the modified Xception model to classify the histological images of breast cancer in the ICIAR 2018 dataset into four classes: invasive carcinoma, in situ carcinoma, benign, and normal. The proposed approach has obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. Our work has been compared with previous works.

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

“…After LLP feature enhancement techniques are applied the LPP-enhanced features are concatenated with the original features obtained from the base network which aims to preserve both the enhanced discriminative information and the original representation captured by the base network and the concatenation operation combines the feature maps of the LPP-enhanced features and the original features, resulting in a combined feature representation. Following the feature concatenation, the modified network integrates a classifier to perform breast cancer detection and classification [43][44][45][46][47][48][49][50][51][52]. In this paper, we use SoftMax classifiers and trained on the combined features, enabling it to learn the discriminative patterns and make accurate predictions.…”

“…2. The figure has four parts, the preprocessing part, the feature extraction [49] part by the inception model, and feature enhancement part by the LLP and the classifier part by SoftMax function. From the proposed system architecture in Fig.…”

Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach

“…In this paper focused on Breast cancer, [1] seeks to address this issue by reducing the impact of ImageNet by utilizing novel approaches for transfer learning and the availability of unlabeled medical photos of the same illness. In order to categorize the histological images of breast cancer in the ICIAR 2018 dataset into four classes invasive carcinoma, in situ carcinoma, benign, and normal the suggested method was applied to the modified Xception model.…”

“…That leaves open, nevertheless, the fact that the features taken from ImageNet are not medical. [1]. The process of Computer Assisted Diagnostics (CAD) and medical image interpreting is largely driven by machine learning.…”

Estimation of Abnormal Cell Growth and MCG-Based Discriminative Feature Analysis of Histopathological Breast Images

“…Finally, the ANN was used to classify the features. Te authors proposed modifed Xception model to resolve the overftting problem and improve the classifcation accuracy [10]. Te methods perform well for breast cancer histopathological images.…”