University Malaysia of Computer Science & Engineering (UNIMY), Cyberjaya, Malaysia.
Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine.
Asian Pac J Cancer Prev. 2023 Feb 1;24(2):531-544. doi: 10.31557/APJCP.2023.24.2.531.
Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images. The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC).
Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy.
The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively.
Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.
早期发现和精确诊断乳腺癌（BC）对于提高 30 至 50 岁患者的诊断水平和改善乳腺癌生存率至关重要。通过医疗保健技术的进步，深度学习在处理和检查大量 X 射线、MRI、CTR 图像方面发挥着重要作用。本研究旨在提出一种深度学习模型（BCCNN），用于将乳腺癌检测和分类为 8 个类别：良性腺病（BA）、良性纤维腺瘤（BF）、良性叶状肿瘤（BPT）、良性管状腺瘤（BTA）、恶性导管癌（MDC）、恶性小叶癌（MLC）、恶性粘液癌（MMC）和恶性乳头状癌（MPC）。
使用提出的深度学习模型对乳腺癌 MRI 图像进行分类，BA、BF、BPT、BTA、MDC、MLC、MMC 和 MPC，该模型还包含 5 个经过微调的深度学习模型，包括基于 ImageNet 数据库训练的 Xception、InceptionV3、VGG16、MobileNet 和 ResNet50。数据集从 Kaggle 存储库中收集用于乳腺癌检测和分类。该数据集使用 GAN 技术进行了增强。数据集中的图像有 4 个放大倍数（40X、100X、200X、400X 和完整数据集）。因此，我们分别使用每个数据集评估了提出的深度学习模型和 5 个预训练模型。这意味着我们总共进行了 30 次实验。在评估所有模型时使用的度量包括：F1 分数、召回率、精度、准确性。
Xception、InceptionV3、ResNet50、VGG16、MobileNet 和提出的模型（BCCNN）的分类 F1 分数准确率分别为 97.54%、95.33%、98.14%、97.67%、93.98%和 98.28%。
数据集增强、预处理和平衡对提高提出的模型（BCCNN）和微调的预训练模型的乳腺癌检测和分类准确率起到了很好的作用。由于 MRI 图像具有较高的分辨率，因此在使用 400X 放大倍数的图像时，获得了最佳的准确率。
