A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 5023 - 5052

Published: July 7, 2023

Language: Английский

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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Current Research in Biotechnology, Journal Year: 2023, Volume and Issue: 7, P. 100164 - 100164

Published: Nov. 22, 2023

The medicine and healthcare sector has been evolving advancing very fast. advancement initiated shaped by the applications of data-driven, robust, efficient machine learning (ML) to deep (DL) technologies. ML in medical is developing quickly, causing rapid progress, reshaping medicine, improving clinician patient experiences. technologies evolved into data-hungry DL approaches, which are more robust dealing with data. This article reviews some critical data-driven aspects intelligence field. In this direction, illustrated recent progress science using two categories: firstly, development data uses and, secondly, Chabot particularly on ChatGPT. Here, we discuss ML, DL, transition requirements from DL. To science, illustrate prospective studies image data, newly interpretation EMR or EHR, big personalized dataset shifts artificial (AI). Simultaneously, recently developed DL-enabled ChatGPT technology. Finally, summarize broad role significant challenges for implementing healthcare. overview paradigm shift will benefit researchers immensely.

Language: Английский

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Recent trend in medical imaging modalities and their applications in disease diagnosis: a review

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(14), P. 43035 - 43070

Published: Oct. 16, 2023

Language: Английский

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BreastHybridNet: A Hybrid Deep Learning Framework for Breast Cancer Diagnosis Using Mammogram Images

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 25, 2025

As a common malignancy in females, breast cancer represents one of the most serious threats to female's life, which is also closely associated with Sustainable Development Goal 3 (SDG 3) United Nations for keeping healthy lives and promoting well-being all people. Breast accounts highest number mortality early diagnosis key reducing disease-specific general. Current methods struggle accurately localize important regions, model sequential dependencies, or combine different features despite considerable improvements artificial intelligence deep learning domains. They prevent diagnostic frameworks from being reliable scalable, especially low-resourced healthcare settings. This study proposes novel hybrid framework, BreastHybridNet, using mammogram images tackle these mutual challenges. The proposed framework combines pre-trained CNN backbone feature extraction, spatial attention mechanism automatically highlight image area, contains signature patterns carrying information, BiLSTM layer obtain dependencies features, fusion strategy process complementarily. Experimental results show that accuracy 98.30%, outperforms state-of-the-art LMHistNet, BreastMultiNet, DOTNet 2.0 extent quantitatively. BreastHybridNet works towards feasibility interpretability scalability on existing systems while contributing worldwide efforts alleviate cancer-related cost-efficient lenses. highlights need AI-enabled solutions contribute accessing technologies screening.

Language: Английский

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Breast cancer classification based on hybrid CNN with LSTM model

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 5, 2025

Breast cancer (BC) is a global problem, largely due to shortage of knowledge and early detection. The speed-up process detection classification crucial for effective treatment. Medical image analysis methods computer-aided diagnosis can enhance this process, providing training assistance less experienced clinicians. Deep Learning (DL) models play great role in accurately detecting classifying the huge dataset, especially when dealing with large medical images. This paper presents novel hybrid model DL combined Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) binary breast on two datasets available at Kaggle repository. CNNs extract mammographic features, including spatial hierarchies malignancy patterns, whereas LSTM networks characterize sequential dependencies temporal interactions. Our method combines these structures improve accuracy resilience. We compared proposed other models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, RESNET-50. CNN-LSTM achieved superior performance accuracies 99.17% 99.90% respective datasets. uses prediction evaluation metrics accuracy, sensitivity, specificity, F-score, AUC curve. results showed that our classifiers others second dataset.

Language: Английский

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Breast Cancer Detection and Classification Empowered With Transfer Learning

Frontiers in Public Health, Journal Year: 2022, Volume and Issue: 10

Published: July 4, 2022

Cancer is a major public health issue in the modern world. Breast cancer type of that starts breast and spreads to other parts body. One most common types kill women cancer. When cells become uncontrollably large, develops. There are various The proposed model discussed benign malignant In computer-aided diagnosis systems, identification classification using histopathology ultrasound images critical steps. Investigators have demonstrated ability automate initial level tumor throughout last few decades. can be detected early, allowing patients obtain proper therapy thereby increase their chances survival. Deep learning (DL), machine (ML), transfer (TL) techniques used solve many medical issues. several scientific studies previous literature on categorization tumors models but with some limitations. However, research hampered by lack dataset. methodology created help automatic Our main contribution technique three datasets, A, B, C, A2, A2 dataset A two classes. this study, used. work customized CNN-AlexNet, which was trained according requirements datasets. This also one contributions work. results shown system empowered achieved highest accuracy than existing datasets A2.

Language: Английский

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Nanovaccines: An effective therapeutic approach for cancer therapy

Biomedicine & Pharmacotherapy, Journal Year: 2023, Volume and Issue: 170, P. 115992 - 115992

Published: Dec. 9, 2023

Cancer vaccines hold considerable promise for the immunotherapy of solid tumors. Nanomedicine offers several strategies enhancing vaccine effectiveness. In particular, molecular or (sub) cellular can be delivered to target lymphoid tissues and cells by nanocarriers nanoplatforms increase potency durability antitumor immunity minimize negative side effects. Nanovaccines use nanoparticles (NPs) as carriers and/or adjuvants, offering advantages optimal nanoscale size, high stability, ample antigen loading, immunogenicity, tunable presentation, increased retention in lymph nodes, promotion. To induce immunity, cancer rely on tumor antigens, which are administered form entire cells, peptides, nucleic acids, extracellular vesicles (EVs), cell membrane–encapsulated NPs. Ideal stimulate both humoral while overcoming tumor-induced immune suppression. Herein, we review key properties nanovaccines highlight recent advances their development based structure composition various (including synthetic semi (biogenic) nanocarriers. Moreover, discuss cell–derived those whole-tumor-cell components, EVs, NPs, hybrid membrane–coated NPs), nanovaccine action mechanisms, challenges immunocancer therapy translation clinical applications.

Language: Английский

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RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 10, 2024

Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection exacerbated by the dimensionality and complexity gene expression data, which complicates classification process.

Language: Английский

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Fractal texture analysis for automated breast cancer detection

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3159, P. 020032 - 020032

Published: Jan. 1, 2025

Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation V. Vijaya Chamundeeswari, Gowri; Fractal texture analysis for automated breast cancer detection. AIP Conf. Proc. 9 January 2025; 3159 (1): 020032. https://doi.org/10.1063/5.0247044 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Dropdown Menu input auto suggest filter your All ContentAIP Publishing PortfolioAIP Conference Proceedings Advanced |Citation

Language: Английский

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Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e1054 - e1054

Published: Aug. 8, 2022

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast survival chances can be improved by early detection diagnosis. For medical image analyzers, diagnosing tough, time-consuming, routine, repetitive. Medical analysis could useful method for detecting such Recently, artificial intelligence technology has been utilized help radiologists identify more rapidly reliably. Convolutional neural networks, among other technologies, are promising recognition classification tools. This study proposes framework automatic reliable based on histological ultrasound data. The system built CNN employs transfer learning metaheuristic optimization. Manta Ray Foraging Optimization (MRFO) approach deployed improve the framework's adaptability. Using Cancer Dataset (two classes) Ultrasound (three-classes), eight modern pre-trained architectures examined apply technique. uses MRFO performance of optimizing their hyperparameters. Extensive experiments have recorded parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, cosine similarity. proposed scored 97.73% histopathological data 99.01% in terms accuracy. experimental results show that superior state-of-the-art approaches literature review.

Language: Английский

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