| Full text | |
| Author(s): |
Pereira, Juliana Wolf
;
Ribeiro, Marcela Xavier
;
Shen, L
;
Gonzalez, AR
;
Santosh, KC
;
Lai, Z
;
Sicilia, R
;
Almeida, JR
;
Kane, B
Total Authors: 9
|
| Document type: | Journal article |
| Source: | 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2022-01-01. |
| Abstract | |
Deep Learning has become increasingly frequent in the studies and analysis of medical images. Advances relevant to this area of research improve computer-aided diagnostic systems and help physicians' routine when providing a second opinion. Breast cancer is one of the types most common cancer among women worldwide. Early diagnosis of breast cancer can facilitate treatment and help saves lives. Mammography is the most widely used exam in the clinical routine to diagnose breast cancer. The analysis of the mammogram requires a specialist with experience in medical imaging. Deep Learning and Machine Learning techniques can collaborate computationally with this task. Adapting the hyperparameters provided to deep learning architectures helps improve the results in analyzing and classifying mammogram images. This paper presents a deep learning-based approach to classifying mammogram image regions of interest (ROIs). This approach includes transfer learning, hyperparameter and fine-tuning, and an ensemble with the models that showed the best results. The process demonstrated promising results, with the ensemble reaching 92% accuracy in the classification of mammogram ROIs of the test set and the area under the curve (AUC) value of 0.97 for the best model. (AU) | |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |