The Role of Artificial Intelligence in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Published: Oct. 8, 2022

Breast cancer is among the most common and fatal diseases for women, no permanent treatment has been discovered. Thus, early detection a crucial step to control cure breast that can save lives of millions women. For example, in 2020, more than 65% patients were diagnosed early-stage cancer, from whom all survived cancer. Although effective approach treatment, screening conducted by radiologists very expensive time-consuming. More importantly, conventional methods analyzing images suffer high false rates. Different imaging modalities are used extract analyze key features affecting diagnosis These be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination them. Radiologists pathologists produced these manually leads increase risk wrong decisions detection. utilization new automatic kinds assist interpret required. Recently, artificial intelligence (AI) widely utilized automatically improve different types specifically thereby enhancing survival chance patients. Advances AI algorithms, deep learning, availability datasets obtained various have opened an opportunity surpass limitations current analysis methods. In this article, we first review modalities, their strengths limitations. Then, explore summarize recent studies employed using modalities. addition, report available on which important developing AI-based algorithms training learning models. conclusion, paper tries provide comprehensive resource help researchers working analysis.

Language: Английский

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EfficientNets transfer learning strategies for histopathological breast cancer image analysis

Advances in Complex Systems, Journal Year: 2023, Volume and Issue: 15(02)

Published: April 5, 2023

Breast cancer (BC) is one of the major principal sources high mortality among women worldwide. Consequently, early detection essential to save lives. BC can be diagnosed with different modes medical images such as mammography, ultrasound, computerized tomography, biopsy, and magnetic resonance imaging. A histopathology study (biopsy) that results in often performed help diagnose analyze BC. Transfer learning (TL) a machine-learning (ML) technique reuses method initially built for task applied model new task. TL aims enhance assessment desired learners by moving knowledge contained another but similar source domain. challenge small dataset domain reduced build learners. plays role image analysis because this immense property. This paper focuses on use methods investigation classification detection, preprocessing, pretrained models, ML models. Through empirical experiments, EfficientNets neural network architectures models were built. The support vector machine eXtreme Gradient Boosting (XGBoost) learned dataset. result showed comparative good performance EfficientNetB4 XGBoost. An outcome based accuracy, recall, precision, F1_Score XGBoost 84%, 0.80, 0.83, 0.81, respectively.

Language: Английский

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Breast Cancer Prediction by Ensembling Machine Learning Algorithms and Explainable AI

Published: March 1, 2024

A primary cause of death is cancer, which a result abnormal cell growth. Globally, breast cancer significant contributor to female fatalities, and its prevention challenging due unidentified causes. However, early detection pivotal for reducing risk improving survival rates. Advanced imaging techniques like mammography ultrasound are instrumental in diagnosing cancer. This model integrates machine learning Explainable AI predict Trained on dataset with diverse features from fine needle aspiration masses, the not only determines whether patient positive or negative but also sheds light importance specific cancerous cell. In cases diagnosis, empowers patients promptly seek essential treatment, significantly enhancing their chances survival.

Language: Английский

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Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets

Measurement, Journal Year: 2023, Volume and Issue: 225, P. 114059 - 114059

Published: Dec. 22, 2023

Language: Английский

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A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108910 - 108910

Published: July 19, 2024

Language: Английский

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Identification and Classification of Breast Cancer using Multilayer Perceptron Techniques for Histopathological Image

Published: March 2, 2023

Among the many types of cancer that affect women, breast (BC) is one most well-known. By analyzing and predicting BC, condition can be effectively treated by preventing future medical issues. Machine learning (ML) often regarded as suitable approach for BC detection due to its effectiveness in complex datasets. Researchers rely on classification make sense vast amounts data they collect their quest identify cancer. To distinction between benign malignant cancers without resorting invasive surgery, a precise consistent diagnostic required early detection. The model trained using obtained from Kaggle database. Multilayer Perceptron able classify with an accuracy 85%. Our anticipated algorithm's primary function illness categorization diagnosis. When used conjunction other methods, MLP improves likelihood diagnosis being made time patient receive treatment when it effective.

Language: Английский

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Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance

Medical & Biological Engineering & Computing, Journal Year: 2023, Volume and Issue: 62(3), P. 901 - 912

Published: Dec. 12, 2023

Language: Английский

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Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging

Cancers, Journal Year: 2022, Volume and Issue: 14(24), P. 6159 - 6159

Published: Dec. 14, 2022

Medical imaging has attracted growing interest in the field of healthcare regarding breast cancer (BC). Globally, BC is a major cause mortality amongst women. Now, examination histopathology images medical gold standard for diagnoses. However, manual process microscopic inspections laborious task, and results might be misleading as result human error occurring. Thus, computer-aided diagnoses (CAD) system can utilized accurately detecting within essential time constraints, earlier diagnosis key to curing cancer. The classification utilizing deep learning algorithm gained considerable attention. This article presents model an improved bald eagle search optimization with synergic mechanism using histopathological (IBESSDL-BCHI). proposed IBESSDL-BCHI concentrates on identification HIs. To do so, presented follows image preprocessing method median filtering (MF) technique step. In addition, feature extraction (SDL) carried out, hyperparameters related SDL are tuned by use IBES model. Lastly, long short-term memory (LSTM) was precisely categorize HIs into two classes, such benign malignant. performance validation tested benchmark dataset, demonstrate that shown better general efficiency classification.

Language: Английский

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Transfer Learning Based Breast Cancer Detection for Telemedicine Systems in Healthare Environment

Published: May 14, 2024

Language: Английский

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Breast Cancer Image Classification: Leveraging Deep Learning and Large Language Models for Semantic Integration

Published: March 15, 2024

Language: Английский

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