Open AccessApplying the Deep Learning Model on an IoT Board for Breast Cancer Detection based on Histopathological Images
Author(s) -
Shahirah Binti Zahir,
Amiza Amir,
Nik Adilah Hanin Zahri,
Wei Chern Ang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1755/1/012026
Subject(s) - transfer of learning , deep learning , convolutional neural network , artificial intelligence , computer science , breast cancer , process (computing) , histopathological examination , machine learning , cancer , pattern recognition (psychology) , pathology , medicine , operating system
In breast cancer diagnosis, pathologists evaluate microscopic images of tissue samples to determine if it is benign or malignant. The manual examination process could result in delayed diagnosis, which leads to late cancer treatment and can risk lives. In this paper, we proposed an automated, low-cost, and portable breast cancer detection based on histopathological images by using deep learning. The deep learning models were designed by using the Convolutional Neural Network (CNN). This paper compares the performance of the CNN model by using transfer learning utilizing a pre-trained model (VGG16) and the performance of a CNN model without transfer learning. The result shows that transfer learning provides a good base for classification of histopathological images. The model was successfully deployed on a Raspberry Pi, which demonstrates the model efficiency to run on a lightweight and portable processor.