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Detecting breast cancer using artificial intelligence: Convolutional neural network.

Avishek Choudhury¹, Sunanda Perumalla²

¹School of Systems and Entereprises, Stevens Institute of Technology, Hoboken, NJ, USA.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|May 24, 2020

Summary

This summary is machine-generated.

This study demonstrates that a convolutional neural network (CNN) can effectively classify breast cancer images, achieving 78.4% accuracy. Further research is needed to improve generalization for this AI-driven diagnostic approach.

Keywords:

Convolutional neural network artificial intelligence breast cancer deep learning ductal carcinoma machine learning

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Area of Science:

Oncology
Computer Science
Medical Imaging

Background:

Pathologist visual inspection of bio-markers is crucial for cancer treatment.
Deep learning enhances computer-aided diagnosis for automated identification of cancerous tissues.
Nuclear structure analysis is vital for cancer detection, with AI accelerating the process.

Purpose of the Study:

To propose and implement an image classification technique for breast cancer identification.
To utilize artificial intelligence for automated breast cancer diagnosis.

Main Methods:

Implementation of a convolutional neural network (CNN).
Application of the CNN model to a breast cancer image dataset.
Identification of invasive ductal carcinoma (IDC) using the CNN.

Main Results:

The CNN model achieved 78.4% classification accuracy after data augmentation.
16% of negative IDC cases were misclassified as false negatives.
25% of positive IDC cases were misclassified as false positives.

Conclusions:

Convolutional neural networks are feasible for breast cancer classification tasks.
AI algorithm performance is dataset-dependent, potentially limiting model generalization.
Further development is required to enhance the robustness and generalizability of AI models in breast cancer diagnosis.