ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning

Deepraj Chowdhury, Anik Das, Ajoy Dey, Shreya Sarkar, Ashutosh Dhar Dwivedi*, Raghava Rao Mukkamala, Lakhindar Murmu

*Corresponding author af dette arbejde

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Abstrakt

Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.
OriginalsprogEngelsk
Artikelnummer832
TidsskriftSensors
Vol/bind22
Udgave nummer3
Antal sider20
ISSN1424-8220
DOI
StatusUdgivet - feb. 2022

Emneord

  • Breast cancer
  • Artificial intellegence
  • Noninvasive detection
  • Deep learning
  • Transfer learning

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