Breast Cancer Detection and Classification Using Deep CNN Techniques

Abstract

Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition, these preprocessed images were first fed to deep convolution neural networks, namely GoogleNet and AlexNet for Adam. Root mean square propagation (RMSprop) and stochastic gradient descent with momentum (SGDM) optimizers were used for different learning rates, such as 0.01, 0.001, and 0.0001. As medical imaging necessitates the presence of discriminative features for classification, the pretrained GoogleNet architectures extract the complicated features from the image and increase the recognition rate. In the latter part of this study, particle swarm optimization-based multi-layer perceptron (PSO-MLP) and ant colony optimization-based multi-layer perceptron (ACO-MLP) were employed for breast cancer recognition using statistical features, such as skewness, kurtosis, variance, entropy, contrast, correlation, energy, homogeneity, and mean, which were extracted from the preprocessed image. The performance of GoogleNet was compared with AlexNet, PSO-MLP, and ACO-MLP in terms of accuracy, loss rate, and runtime and was found to achieve an accuracy of 99% with a lower loss rate of 0.1547 and the lowest run time of 4.14 minutes.