Automated Breast Cancer Detection in Mammograms using Transfer Learningbased Deep Learning Models
Introduction: When considering cancer mortality rates in general, Breast Cancer (BC) is a major contributor among females. Patients’ chances of survival increase when BC is detected early and treated with the appropriate treatment at the right time. There is strong evidence that mammography, when us...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JCDR Research and Publications Private Limited
2025-01-01
|
| Series: | Journal of Clinical and Diagnostic Research |
| Subjects: | |
| Online Access: | https://jcdr.net/articles/PDF/20532/75303_CE(Ra1)_F(Sh)_QC(AN_IS)_PF1(RI_OM_SS)_PFA(IS)_PB(RI_IS)_PN(IS).pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Introduction: When considering cancer mortality rates in general, Breast Cancer (BC) is a major contributor among females. Patients’ chances of survival increase when BC is detected early and treated with the appropriate treatment at the right time. There is strong evidence that mammography, when used as a screening tool, can detect BC at an early stage. Mammography is a diagnostic tool that uses low-dose X-rays to visualise the breast and evaluate its anatomy. For screening purposes, it is currently the preferred method. The present study employs deep learning models trained using Transfer Learning (TL) techniques.
Aim: To automate the process of BC diagnosis in mammograms. The main goal of this approach is to simplify the process of early detection and diagnosis of BC for healthcare practitioners.
Materials and Methods: The dataset obtained from the Mammographic Image Analysis Society (MIAS) was categorised into three distinct categories: benign, malignant and normal. The initial MIAS dataset underwent several preprocessing techniques, including noise reduction, breast image contrast enhancement, non breast region deletion and malignant lesion identification, before analysis. An intricately designed fully connected classifier complements pretrained Convolutional Neural Network (CNN) architectures like ResNet50 and VGG16 in the proposed model.
Results: The VGG16 model performed admirably, achieving an Area Under the Curve (AUC) of 0.950 and an accuracy rate of 96.00%. In addition, it displayed an outstanding F-score of 97%, along with high sensitivity, specificity and accuracy. These outcomes are significantly better compared to the other methods.
Conclusion: The model’s enhanced capabilities for early-stage cancer detection could improve patient outcomes and reduce mortality rates. Furthermore, new tools can ease the workload for radiologists, leading to more standardised and efficient diagnostic procedures. |
|---|---|
| ISSN: | 2249-782X 0973-709X |