An Efficient Method for Breast Mass Classification Using Pre-Trained Deep Convolutional Networks

Abstract

Masses are the early indicators of breast cancer, and distinguishing between benign and malignant masses is a challenging problem. Many machine learning- and deep learning-based methods have been proposed to distinguish benign masses from malignant ones on mammograms. However, their performance is not satisfactory. Though deep learning has been shown to be effective in a variety of applications, it is challenging to apply it for mass classification since it requires a large dataset for training and the number of available annotated mammograms is limited. A common approach to overcome this issue is to employ a pre-trained model and fine-tune it on mammograms. Though this works well, it still involves fine-tuning a huge number of learnable parameters with a small number of annotated mammograms. To tackle the small set problem in the training or fine-tuning of CNN models, we introduce a new method, which uses a pre-trained CNN without any modifications as an end-to-end model for mass classification, without fine-tuning the learnable parameters. The training phase only identifies the neurons in the classification layer, which yield higher activation for each class, and later on uses the activation of these neurons to classify an unknown mass ROI. We evaluated the proposed approach using different CNN models on the public domain benchmark datasets, such as DDSM and INbreast. The results show that it outperforms the state-of-the-art deep learning-based methods.

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

“…This technique improves the information about breast mass. (Ull et al, 2023;Bandaru et al, 2022a;Duong et al, 2023;Kahnouei et al, 2022;P and V, 2022;Li et al, 2019aLi et al, , 2021cMahmood et al, 2022;Wang et al, 2022;Yao et al, 2022), (Tsochatzidis et al, 2021;Ahmed et al, 2020;Fathy and Ghoneim, 2019;Frazer et al, 2021;Sannasi Chakravarthy and Rajaguru, 2021;Chakravarthy and Rajaguru, 2022;Al-Antari et al, 2020;Al-Mansour et al, 2022;Alruwaili and Gouda, 2022;Altameem et al, 2022;Altaf, 2021;Cao et al, 2020;Al-Tam et al, 2022;Bandaru et al, 2022b;López-Cabrera et al, 2020;Saber et al, 2021;Chougrad et al, 2018;Falconi et al, 2020;Jafarzadeh Ghoushchi et al, 2021;Hanis et al, 2023;Lin et al, 2022;Mokni and Haoues, 2022;Mudeng et al, 2022;Khan and Masala, 2023;Oza et al, 2023;Prodan et al, 2023;Ragab et al, 2021;Yu et al, 2023b;Gerbasi et al, 2023;Shanker and Vadivel, 2022;Zhang and Wang, 2019;Adedigba et al, 2022;…”

Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

“…Mass detection and classification have been performed by Momminet-V2 using multi-view mammograms [26]. The computational cost of deep learning model training is becoming a concern, and recently breast cancer mass pathology was classified by implementing pre-trained deep neural networks without transfer learning [27]. NASNet is a high performing CNN for image classification on the ImageNet [28], [29].…”

Intelligent Breast Mass Classification Approach Using Archimedes Optimization Algorithm with Deep Learning on Digital Mammograms

“…The use of automated CAD methods with mammograms increases the accuracy rate of identification; the operational expedient accelerates the diagnosing process and retains the medical appliances. Additionally, the breast mass shows unique symptoms in BC detection [4]. The marginal data of the breast mass display the growth patterns and biological features.…”