Breast Cancer Detection Using Autoencoder with Convolutional Neural Network
S. Ranjana, A. Meenakshi · 2024
Breast cancer is one of the most common cancers in human's life that frequently turn up in women as well as rarely in men too. The majority of the time, the results of the biopsy are used to identify the malignancy and aid in establishing its stage. Convolution neural networks (CNN) and other Deep Learning (DL) architectures are mainly utilized for image classification. This even works well for classifying images of breast cancer. There are currently various feature extraction mechanisms available. Additionally, the CNN is utilized for feature extraction and image classification. During deployment stage, the adaptors have trained in transforming the test image as well as its features for minimizing the domain shift has been measured through the Convolutional Autoencoder (CAE) reconstruction loss. This research has concentrated in building a model is adapted with a single test that subjected at inference and the proposed model has adopted neural networks as an AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors that have been optimized at the testing stage using a single test subject for effective computation. Therefore, this study has used VGG16 and VGG19 with CNN to extract features from BreaKHis database that involves images of microscopic biopsy to benign as well as malignant breast tumors for performing analysis of the unsupervised images. The evaluated results showed that accuracy of CAE with CNN-VGG16 has high accuracy as 96.17% in training that indicates DL models have appropriate in detection and classification of the breast cancers precisely.