Breast Cancer Detection fromHistopathological Images using DeepLearning and Transfer Learning
Year: 2020
Type: dissertation
Abstract: Breast Cancer is the most common cancer in women and it's harming women's mental and physical health. Due to complexities present in Breast Cancer images, image processing technique is required in the detection of cancer. Early detection of Breast cancer required new deep learning and transfer learning techniques. In this paper, histopathological images are used as a dataset from Kaggle. Images are processed using histogram normalization techniques. This research project implements the Convolutional Neural Network(CNN) model based on deep learning and DenseNet-121 based on transfer-learning. Transfer learning uses the Imagenet pre-trained model for training. Hyper-parameter tuning is done for increasing accuracy and precision value. Research achieved 90.9 % test accuracy using the CNN model and 88.03 % accuracy by the transfer learning model.
Source: NORMA
Author: Mansi Chowkkar
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3041001745