EARLY DETECTION OF BREAST CANCER USING DEEP NEURAL NETWORK CLASSIFICATION MODELS ON HISTOPATHOLOGICAL IMAGES

Abstract

Breast cancer is the most common type of cancer, with over 2.2 million cases reported in 2020. Breast cancer treatment can be highly effective, especially when the disease can be detected at an early stage. Nowadays, scientists provide many solutions to identify the type of tumor in the early stages through medical imaging. In this study, our task is to use pre-trained deep CNN architectures to find the type of tumor and develop these models using different optimization methods, changing the parameters of settings for these models, and applying data augmentations methods to the medical images which in turn, yield models with better accuracy. During this work, we used six different models on five datasets from the same database and improved the results through different data augmentation methods. A database of histopathological images was used, where the data are divided into malignant, benign, and pre-trained models were also used as Efficientnetv2, Mobilenet-v2, Resnetv2- 50, VGG16, Inception-v3, and Inception-Resnet-v2 Almost all models performed well after fine-tuning histopathological images, but EfficientNet showed the best result with 94.5% accuracy. In addition, it performed well on magnified under a microscope 200x histopathological images and achieved 98% accuracy.