Montaha Sidratul, Azam Sami, Rafid Abul Kalam Muhammad Rakibul Haque, Ghosh Pronab, Hasan Md Zahid, Jonkman Mirjam, De Boer Friso
Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh.
College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT 0909, Australia.
Biology (Basel). 2021 Dec 17;10(12):1347. doi: 10.3390/biology10121347.
Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone.
Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies.
Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%.
Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.
早期识别和治疗乳腺癌可降低死亡率。目前，乳房X线摄影是乳腺癌检测中使用最广泛的有效成像技术。然而，乳房X线摄影的错误解读可能导致误诊率，因为区分癌性肿块与相邻组织往往很复杂且容易出错。
评估了六种预训练和微调的深度卷积神经网络（CNN）架构：VGG16、VGG19、MobileNetV2、ResNet50、DenseNet201和InceptionV3，以确定哪种模型性能最佳。我们提出了一种以VGG16为基础的BreastNet18模型，因为VGG16的准确率最高。对BreastNet18进行了消融研究，以评估其鲁棒性并实现尽可能高的准确率。采用各种具有合适参数值的图像处理技术来去除伪影并提高图像质量。使用七种增强技术对1442张预处理乳房X线照片的总数据集进行了增强，得到了一个包含11536张图像的数据集。为了研究可能的过拟合问题，进行了k折交叉验证。然后在有噪声的乳房X线照片上测试该模型，以评估其鲁棒性。将结果与先前的研究进行了比较。
提出的BreastNet18模型表现最佳，训练准确率为96.72%，验证准确率为97.91%，测试准确率为98.02%。相比之下，VGGNet19的测试准确率为96.24%，MobileNetV2为77.84%，ResNet50为79.98%，DenseNet201为86.92%，InceptionV3为76.87%。
我们提出的基于图像处理、迁移学习、微调及消融研究的方法，在处理数量有限的复杂医学图像时，展现出了较高的乳腺癌正确分类率。
