A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.
Phys Med
; 107: 102560, 2023 Mar.
Article
em En
| MEDLINE
| ID: mdl-36878133
ABSTRACT
PURPOSE:
Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions. While breast biopsy is referred to as the "gold standard" in assessing both the activity and degree of breast cancer, it is an invasive and time-consuming approach.METHOD:
The current study's primary objective was to develop a novel deep-learning architecture based on the InceptionV3 network to classify ultrasound breast lesions. The main promotions of the proposed architecture were converting the InceptionV3 modules to residual inception ones, increasing their number, and altering the hyperparameters. In addition, we used a combination of five datasets (three public datasets and two prepared from different imaging centers) for training and evaluating the model.RESULTS:
The dataset was split into the train (80%) and test (20%) groups. The model achieved 0.83, 0.77, 0.8, 0.81, 0.81, 0.18, and 0.77 for the precision, recall, F1 score, accuracy, AUC, Root Mean Squared Error, and Cronbach's α in the test group, respectively.CONCLUSIONS:
This study illustrates that the improved InceptionV3 can robustly classify breast tumors, potentially reducing the need for biopsy in many cases.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Aprendizado Profundo
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Screening_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
Phys Med
Assunto da revista:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Irã