Breast cancer; Computer-aided diagnosis; Convolutional Neural Network; Image processing; Transfer learning; Breast Cancer; Breast cancer diagnosis; Computer aided diagnosis systems; Convolutional neural network; Images processing; Learning techniques; Mammogram images; Modelling systems; Multi-views; Artificial Intelligence; Computer Vision and Pattern Recognition; Signal Processing; Computer Science Applications; Radiology, Nuclear Medicine and Imaging
Abstract :
[en] Breast cancer is the main lead of women's cancer-related mortalities. Therefore, early detection is imperative for preventing breast cancer from developing to advanced stages. Moreover, the emergence of Computer-aided diagnosis systems combined with Deep Learning techniques improve breast cancer diagnosis at its early stages. This study proposes a system for classifying mammogram images scanned from different views based on Deep learning techniques. In this paper, we propose combining four pre-trained models in one system employing the Transfer Learning technique with different input images of the same lesion. This proposed system provides a binary classification of the lesion into malignant or benign classes. The Mini-DDSM dataset images were used in this study; the images were pre-processed and augmented to achieve the best results. Several well-known pre-trained models for image classification (ResNet, Xception, DenseNet, MobileNet, and Inception) were experimented with for the proposed model construction. The study results showed that our proposed model achieved an accuracy of 94.18%, a precision of 94.26%, a recall of 94.16%, and an F1-Score of 94.21%, outperforming the pre-trained model on classifying mammogram images of the same dataset.
Disciplines :
Computer science
Author, co-author :
Kacher, Abdelhafidh; University of Tiaret, LIM Research Laboratory, Computer Science Department, Algeria
Merati, Medjeded; University of Tiaret, LIM Research Laboratory, Computer Science Department, Algeria
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
Classification of Multi-view mammogram images using a parallel pre-trained models system
Publication date :
2024
Event name :
2024 8th International Conference on Image and Signal Processing and their Applications (ISPA)
Event place :
Biskra, Dza
Event date :
21-04-2024 => 22-04-2024
Audience :
International
Main work title :
Proceedings - 8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024
Publisher :
Institute of Electrical and Electronics Engineers Inc.
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