Deduction of IDC Using Deep Learning

EasyChair Preprint 6867

Abstract

Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems (CADe)contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Deep learning has the ability to handle such complex situations which include high-dimensional matrix multiplications. Various architectures of CNN were applied and the model with the high generalization accuracy and minimal complexity is selected. The histopathology images are given as input to the CNN network as training models and then finally classified as having IDC or Malignancy. This is generally done by extracting features through a convolutional neural network (CNN) and then classifying using a fully connected networkIn this project in python, we’ll build a classifier to train on 80% of a breast cancer histology image dataset. Of this, we’ll keep 10% of the data for validation. Using Keras, we’ll define a CNN, call it CancerNet, and train it on our images. We’ll then derive a confusion matrix to analyze the performance of the model. Cancer that develops in a milk duct and invades the fibrous or fatty breast tissue outside the duct; it is the most common form of breast cancer forming 80% of all breast cancer diagnoses. And histology is the study of the microscopic structure of tissues.

Keyphrases: CADe - Computer-aided detection, CNN - Convolutional Neural Network, IDC - Invasive Ductal Carcinoma

@booklet{EasyChair:6867,
  author    = {K S Ramya Devi and M Srenidhi and R M Rani},
  title     = {Deduction of IDC Using Deep Learning},
  howpublished = {EasyChair Preprint 6867},
  year      = {EasyChair, 2021}}