Detection of Masses in Mammogram Images Based on the Enhanced RetinaNet Network With INbreast Dataset

Overview

Journal J Multidiscip Healthc

Publisher Dove Medical Press

Specialty Health Services

Date 2025 Feb 12

PMID 39935433

Authors

Mingzhao Wang

Ran Liu

Joseph Luttrell Iv

Chaoyang Zhang

Juanying Xie

Affiliations

Soon will be listed here.

Abstract

Purpose: Breast cancer is the most common major public health problems of women in the world. Until now, analyzing mammogram images is still the main method used by doctors to diagnose and detect breast cancers. However, this process usually depends on the experience of radiologists and is always very time consuming.

Patients And Methods: We propose to introduce deep learning technology into the process for the facilitation of computer-aided diagnosis (CAD), and address the challenges of class imbalance, enhance the detection of small masses and multiple targets, and reduce false positives and negatives in mammogram analysis. Therefore, we adopted and enhanced RetinaNet to detect masses in mammogram images. Specifically, we introduced a novel modification to the network structure, where the feature map M5 is processed by the ReLU function prior to the original convolution kernel. This strategic adjustment was designed to prevent the loss of resolution for small mass features. Additionally, we introduced transfer learning techniques into training process through leveraging pre-trained weights from other RetinaNet applications, and fine-tuned our improved model using the INbreast dataset.

Results: The aforementioned innovations facilitate superior performance of the enhanced RetiaNet model on the public dataset INbreast, as evidenced by a mAP (mean average precision) of 1.0000 and TPR (true positive rate) of 1.00 at 0.00 FPPI (false positive per image) on the INbreast dataset.

Conclusion: The experimental results demonstrate that our enhanced RetinaNet model defeats the existing models by having more generalization performance than other published studies, and it can also be applied to other types of patients to assist doctors in making a proper diagnosis.

References

Hussein H, Abbas E, Keshavarzi S, Fazelzad R, Bukhanov K, Kulkarni S . Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis. Radiology. 2023; 306(3):e221785. DOI: 10.1148/radiol.221785. View

Sahu A, Das P, Meher S . Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Phys Med. 2023; 114:103138. DOI: 10.1016/j.ejmp.2023.103138. View

Zhang Z, Wu H, Zhao H, Shi Y, Wang J, Bai H . A Novel Deep Learning Model for Medical Image Segmentation with Convolutional Neural Network and Transformer. Interdiscip Sci. 2023; 15(4):663-677. DOI: 10.1007/s12539-023-00585-9. View

Kontos D . The promise of AI in personalized breast cancer screening: are we there yet?. Nat Rev Clin Oncol. 2024; 21(6):403-404. DOI: 10.1038/s41571-024-00877-z. View

Lin T, Goyal P, Girshick R, He K, Dollar P . Focal Loss for Dense Object Detection. IEEE Trans Pattern Anal Mach Intell. 2018; 42(2):318-327. DOI: 10.1109/TPAMI.2018.2858826. View

Peng Y, Xu Y, Wang M, Zhang H, Xie J . The nnU-Net based method for automatic segmenting fetal brain tissues. Health Inf Sci Syst. 2023; 11(1):17. PMC: 10043149. DOI: 10.1007/s13755-023-00220-3. View

Kozegar E, Soryani M, Minaei B, Domingues I . Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther. 2014; 9(4):592-600. DOI: 10.4103/0973-1482.126453. View

Xie J, Liu R, Luttrell 4th J, Zhang C . Deep Learning Based Analysis of Histopathological Images of Breast Cancer. Front Genet. 2019; 10:80. PMC: 6390493. DOI: 10.3389/fgene.2019.00080. View

Singh H, Sharma V, Singh D . Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor. Vis Comput Ind Biomed Art. 2022; 5(1):3. PMC: 8752652. DOI: 10.1186/s42492-021-00100-1. View

10.

Yan Y, Conze P, Lamard M, Quellec G, Cochener B, Coatrieux G . Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal. 2021; 71:102083. DOI: 10.1016/j.media.2021.102083. View

11.

Yu X, Kang C, Guttery D, Kadry S, Chen Y, Zhang Y . ResNet-SCDA-50 for Breast Abnormality Classification. IEEE/ACM Trans Comput Biol Bioinform. 2020; 18(1):94-102. DOI: 10.1109/TCBB.2020.2986544. View

12.

Das R, Kaur K, Walia E . Feature Generalization for Breast Cancer Detection in Histopathological Images. Interdiscip Sci. 2022; 14(2):566-581. DOI: 10.1007/s12539-022-00515-1. View

13.

Rehman S, Khan M, Masood A, Almujally N, Baili J, Alhaisoni M . BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images. Diagnostics (Basel). 2023; 13(9). PMC: 10178634. DOI: 10.3390/diagnostics13091618. View

14.

Adedigba A, Adeshina S, Aibinu A . Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset. Bioengineering (Basel). 2022; 9(4). PMC: 9027584. DOI: 10.3390/bioengineering9040161. View

15.

Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S . Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS One. 2018; 13(9):e0203355. PMC: 6143189. DOI: 10.1371/journal.pone.0203355. View

16.

Agarwal R, Diaz O, Yap M, Llado X, Marti R . Deep learning for mass detection in Full Field Digital Mammograms. Comput Biol Med. 2020; 121:103774. DOI: 10.1016/j.compbiomed.2020.103774. View

17.

Lou Q, Li Y, Qian Y, Lu F, Ma J . Mammogram classification based on a novel convolutional neural network with efficient channel attention. Comput Biol Med. 2022; 150:106082. DOI: 10.1016/j.compbiomed.2022.106082. View

18.

Agarwal R, Diaz O, Llado X, Yap M, Marti R . Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging (Bellingham). 2022; 6(3):031409. PMC: 6381602. DOI: 10.1117/1.JMI.6.3.031409. View

19.

Jabeen K, Khan M, Hameed M, AlQahtani O, Alouane M, Masood A . A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images. Front Oncol. 2024; 14:1347856. PMC: 10917916. DOI: 10.3389/fonc.2024.1347856. View

20.

Wang J, Khan M, Wang S, Zhang Y . SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis. Comput Mater Contin. 2024; 76(2):2201-2216. PMC: 7615775. DOI: 10.32604/cmc.2023.041191. View