A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(1), P. 20342 - 20347

Published: Feb. 2, 2025

Cancer is the second leading cause of death globally, with Breast (BC) accounting for 20% new diagnoses, making it a major morbidity and mortality. Mammography effective BC detection, but lesion interpretation challenging, prompting development Computer-Aided Diagnosis (CAD) systems to assist in classification detection. Machine Learning (ML) Deep (DL) models are widely used disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation Recognition Classification (OGCRNN-SBCRC) technique. In preparation phase, images masks annotated then classified as benign or malignant. To achieve this, Wiener Filter (WF)-based noise removal log transform-based contrast enhancement preprocessing. The OGCRNN-SBCRC technique utilizes UNet++ method segmentation RMSProp optimizer parameter tuning. addition, employs ConvNeXtTiny Convolution (CNN) approach feature extraction. For (GCRNN) model used. Finally, Aquila Optimizer (AO) employed hyperparameter tuning GCRNN approach. simulation analysis methodology, using image dataset, demonstrated superior performance accuracy 99.65%, surpassing existing models.

Language: Английский

AI-Assisted Breast Cancer Prediction, Classification, and Future Directions: A Narrative Review Involving Histopathological Image Datasets

The Open Public Health Journal, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 7, 2025

Breast cancer-related deaths in women have increased significantly the past decade, emphasizing need for an accurate and early diagnosis. AI-assisted diagnosis using deep learning machine (DML) approaches has become a key method analysing breast tissue identifying tumour stages. DML algorithms are particularly effective classifying cancer images due to their ability handle large datasets, work with unstructured data, generate automated features, improve over time. However, performance of these models is heavily on datasets used training, performing inconsistently between different datasets. Given prediction that by 2050, there will be more than 30 million new cases 10 worldwide, it crucial focus recent advancements histopathological image systems. Histopathological provide critical information identify abnormalities, which directly impact model performance. This review discusses analyses various DML-based implementation, highlighting research gaps offering suggestions future improvements. The goal develop efficient early-stage cancer. In addition, this detection assists healthcare professional guiding prevention methods smart

Language: Английский