CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.
Cancer Med
; 13(16): e70069, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-39215495
ABSTRACT
OBJECTIVE:
Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification.METHODS:
This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.RESULTS:
It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.CONCLUSIONS:
The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
/
Neural Networks, Computer
/
Carcinoma, Ductal, Breast
/
Deep Learning
Limits:
Female
/
Humans
Language:
En
Journal:
Cancer Med
Year:
2024
Document type:
Article
Affiliation country:
Bangladesh
Country of publication:
United States