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Deep Learning-based Analysis Of Mammographic Images For Breast Cancer Detection Using Transfer Learning.

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Deep Learning-based Analysis Of Mammographic Images For Breast Cancer Detection Using Transfer Learning.

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Related Experiment Video

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Deep Learning-Based Analysis of Mammographic Images for Breast Cancer Detection Using Transfer Learning.

Fajar Walayat¹, Allah Ditta¹, Zafar Iqbal Karmani²

¹Department of Information Sciences University of Education Lahore Pakistan.

Healthcare Technology Letters

|October 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel transfer learning method using convolutional neural networks (CNNs) for early breast cancer detection in mammograms. AlexNet achieved 96.7% accuracy, significantly improving diagnostic capabilities.

Keywords:

convolutional neural networks (CNN)deep learning solvers epoch iterations learning rate mammogram

Related Experiment Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Breast cancer is a leading global health concern, particularly affecting older women.
Early detection is crucial for improving patient survival rates and reducing mortality.
Existing diagnostic methods can be prone to human error, necessitating automated solutions.

Purpose of the Study:

To develop an innovative transfer learning methodology for early breast cancer detection using mammograms.
To enhance diagnostic accuracy and automate the analysis process for medical professionals.
To investigate the efficacy of various convolutional neural network (CNN) architectures for this task.

Main Methods:

A transfer learning approach was applied to mammogram images.
Several CNN architectures, including Inception-v3, ResNet-50, VGG-16, SqueezeNet, and AlexNet, were evaluated.
Image segmentation and noise reduction techniques were employed on a dataset of 900 mammograms from a local hospital.

Main Results:

The AlexNet architecture demonstrated superior performance among the evaluated CNN models.
An outstanding accuracy of 96.7% was achieved using the AlexNet model.
The transfer learning model proved effective in identifying early-stage breast cancer from mammograms.

Conclusions:

The proposed transfer learning methodology significantly enhances breast cancer detection accuracy.
AlexNet, within this framework, offers a potent tool for automated and accurate mammogram analysis.
This approach has the potential to aid clinicians in early diagnosis, improving patient outcomes.