CITATION.cff

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title: >-
  Adamouization/Breast-Cancer-Detection-Mammogram-Deep-Learning-Publication:
  PLOS ONE Submission
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Adam
    family-names: Jaamour
    email: a.jaamour@bath.edu
    orcid: 'https://orcid.org/0000-0002-8298-1302'
    affiliation: University of St Andrews
  - given-names: Craig
    family-names: Myles
    affiliation: University of St Andrews
    orcid: 'https://orcid.org/0000-0002-2701-3149'
identifiers:
  - type: doi
    value: 10.5281/zenodo.7980706
repository-code: >-
  https://github.com/Adamouization/Breast-Cancer-Detection-Mammogram-Deep-Learning-Publication
url: >-
  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0280841
abstract: >-
  Breast cancer claims 11,400 lives on average every year in
  the UK, making it one of the deadliest diseases.
  Mammography is the gold standard for detecting early signs
  of breast cancer, which can help cure the disease during
  its early stages. However, incorrect mammography diagnoses
  are common and may harm patients through unnecessary
  treatments and operations (or a lack of treatment).
  Therefore, systems that can learn to detect breast cancer
  on their own could help reduce the number of incorrect
  interpretations and missed cases. Various deep learning
  techniques, which can be used to implement a system that
  learns how to detect instances of breast cancer in
  mammograms, are explored throughout this paper.
  Convolution Neural Networks (CNNs) are used as part of a
  pipeline based on deep learning techniques. A divide and
  conquer approach is followed to analyse the effects on
  performance and efficiency when utilising diverse deep
  learning techniques such as varying network architectures
  (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2),
  class weights, input sizes, image ratios, pre-processing
  techniques, transfer learning, dropout rates, and types of
  mammogram projections. This approach serves as a starting
  point for model development of mammography classification
  tasks. Practitioners can benefit from this work by using
  the divide and conquer results to select the most suitable
  deep learning techniques for their case out-of-the-box,
  thus reducing the need for extensive exploratory
  experimentation. Multiple techniques are found to provide
  accuracy gains relative to a general baseline (VGG19 model
  using uncropped 512 × 512 pixels input images with a
  dropout rate of 0.2 and a learning rate of 1 × 10−3) on
  the Curated Breast Imaging Subset of DDSM (CBIS-DDSM)
  dataset. These techniques involve transfer learning
  pre-trained ImagetNet weights to a MobileNetV2
  architecture, with pre-trained weights from a binarised
  version of the mini Mammography Image Analysis Society
  (mini-MIAS) dataset applied to the fully connected layers
  of the model, coupled with using weights to alleviate
  class imbalance, and splitting CBIS-DDSM samples between
  images of masses and calcifications. Using these
  techniques, a 5.6% gain in accuracy over the baseline
  model was accomplished. Other deep learning techniques
  from the divide and conquer approach, such as larger image
  sizes, do not yield increased accuracies without the use
  of image pre-processing techniques such as Gaussian
  filtering, histogram equalisation and input cropping.
keywords:
  - machine-learning
  - deep-learning
  - convolutional-neural-network
  - cnn
  - breast-cancer-detection
  - mammogram-classification
  - plos-one
license: BSD-2-Clause
commit: bc82a51cf1105d6bd24a9c35928d7f625eb456ef
version: '1.2'
date-released: '2023-05-29'