Deep Learning for Medical Image-Based Cancer Diagnosis

Cancers, Journal Year: 2023, Volume and Issue: 15(14), P. 3608 - 3608

Published: July 13, 2023

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one the research hotspots in field artificial intelligence and computer vision. Due rapid development methods, requires very high accuracy timeliness as well inherent particularity complexity imaging. A comprehensive review relevant studies necessary help readers better understand current status ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission (PET), histopathological are reviewed this paper. basic architecture classical pretrained models comprehensively reviewed. In particular, advanced neural networks emerging recent years, transfer learning, ensemble (EL), graph network, vision transformer (ViT), introduced. overfitting prevention methods summarized: batch normalization, dropout, weight initialization, data augmentation. image-based analysis sorted out. (3) Results: Deep has achieved great success diagnosis, showing good results image classification, reconstruction, detection, segmentation, registration, synthesis. However, lack high-quality labeled datasets limits role faces challenges rare multi-modal fusion, model explainability, generalization. (4) Conclusions: There a need for more public standard databases cancer. pre-training potential be improved, special attention should paid multimodal fusion supervised paradigm. Technologies such ViT, few-shot will bring surprises images.

Language: Английский

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Exploring the ethical considerations of using Chat GPT in university education

Periodicals of Engineering and Natural Sciences (PEN), Journal Year: 2023, Volume and Issue: 11(4), P. 105 - 105

Published: Aug. 30, 2023

This study investigates the moral dilemmas that arise with incorporating Chat GPT into higher education, a focus on situation in Latinoamerican institutions of learning. The surveyed 220 people via online questionnaire to learn more about their experiences and motivations for using AI-powered conversational agents. An overview demographics participants was provided through descriptive statistics. investigation subject at hand lays groundwork further research. It also reveals hidden meanings observed phenomena, it suggests possible solutions problems have been uncovered. research looks how AI systems chatbots can supplement human knowledge judgment, as well potential drawbacks. results showed thought integration moderately accessible had positive social attitudes. They understood value responsibility creating individualized educational opportunities. Participants stressed necessity explicit institutional standards regarding privacy data security. Gender, age, sense accessibility, attitude, opinions, personal experience, security, guidelines, learning were found affect participants' reliance regression analysis. findings shed light education is complicated by factors such individual beliefs, cultural norms, ethical problems. busy schedules students may be accommodated resources they need succeed made available thanks this adaptability. In addition, natural language processing models offer instantaneous help text chat, voice, or video. To fully grasp consequences lead creation responsible implementation techniques, proposes additional qualitative investigations, longitudinal studies, comparative across diverse contexts required. Closing these gaps will move field forward ways are beneficial classroom.

Language: Английский

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Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review

Human-Centric Intelligent Systems, Journal Year: 2023, Volume and Issue: 3(4), P. 588 - 615

Published: Sept. 11, 2023

Abstract The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum uses like Computational linguistics, image identification, autonomous systems. With the increasing demand for intelligent systems, it become crucial to comprehend different categories machine acquiring knowledge systems along with their applications in present world. This paper presents actual use cases learning, including cancer classification, how algorithms have been implemented on medical data categorize diverse forms anticipate outcomes. also discusses supervised, unsupervised, reinforcement highlighting benefits disadvantages each category intelligence system. conclusions this systematic study methods classification numerous implications. main lesson is that through accurate kinds, patient outcome prediction, identification possible therapeutic targets, holds enormous potential improving diagnosis therapy. review offers readers broad understanding as advancements applied today, empowering them decide themselves whether these clinical settings. Lastly, wraps up by engaging discussion future new types be developed field advances. Overall, information included survey article useful scholars, practitioners, individuals interested gaining about fundamentals its various areas activities.

Language: Английский

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Federated Learning Approach for Breast Cancer Detection Based on DCNN

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 40114 - 40138

Published: Jan. 1, 2024

Breast cancer stands as one of the predominant health challenges globally, affecting millions women every year and necessitating early accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast detection, their application is often hampered by privacy concerns associated with sharing data limitation training on small, localized datasets. Addressing these challenges, this manuscript introduces an effective federated learning approach tailored for leveraging DCNNs across diverse large datasets without compromising privacy. Our experimental findings underscore significant advancements accuracy 98.9% three scale which are VINDR-MAMMO, CMMD, INBREAST. Additionally, we tested proposed performance, showcasing potential our a robust privacy-preserving solution future diagnostic strategies.

Language: Английский

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Pixel-guided pattern alignment based Hopfield Neural Networks for generalize cancer diagnosis

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107397 - 107397

Published: Jan. 7, 2025

Language: Английский

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An Enhanced Optimization of Machine Learning Model in Prostate Cancer Detection

Published: Feb. 9, 2024

the present study goals to optimize overall performance and accuracy of a device-mastering model for prostate most cancers detection. Prostate is malignancy which regularly leaves little no clue its presence. Early detection is, therefore, important thing hit remedy. Gadget mastering models are increasingly more being applied in fitness care prognosis diagnosis. However, those fashions frequently require good sized quantities records well-crafted machines gain ultimate accuracy. The observe proposed desirable Optimization machine (EOML) method enhance cancer version. First, gadget learning changed into educated usage publicly available from Genome Atlas. These facts set become preprocessed, feature extraction choice were executed classical ensemble function selection approach. After that, pass-validation used version further. Eventually, an gaining knowledge approach became adopted model's getting know technique blended predictions some device create robust dependable

Language: Английский

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Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer

Diagnostics, Journal Year: 2023, Volume and Issue: 13(15), P. 2538 - 2538

Published: July 31, 2023

Cervical cancer is one of the most common types malignant tumors in women. In addition, it causes death latter stages. Squamous cell carcinoma and aggressive form cervical must be diagnosed early before progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best commonly used for screening converted from glass slides whole-slide images (WSIs) computer-assisted analysis. Manual diagnosis by microscopes limited prone manual errors, tracking all cells difficult. Therefore, development computational techniques important as diagnosing many samples can done automatically, quickly, efficiently, which beneficial medical laboratories professionals. This study aims develop automated WSI image analysis models squamous dataset. Several systems have been designed analyze accurately distinguish progression. For proposed systems, were optimized show contrast edges low-contrast cells. Then, analyzed segmented isolated rest using Active Contour Algorithm (ACA). hybrid method between deep learning (ResNet50, VGG19 GoogLeNet), Random Forest (RF), Support Vector Machine (SVM) algorithms based on ACA algorithm. Another RF SVM fused features deep-learning (DL) (ResNet50-VGG19, VGG19-GoogLeNet, ResNet50-GoogLeNet). It concluded systems' performance that DL models' combined help significantly improve networks. The novelty this research combines extracted ResNet50-GoogLeNet) with images. results demonstrate SVM. network ResNet50-VGG19 achieved an AUC 98.75%, sensitivity 97.4%, accuracy 99%, precision 99.6%, specificity 99.2%.

Language: Английский

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A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer

Deleted Journal, Journal Year: 2024, Volume and Issue: 37(1), P. 280 - 296

Published: Jan. 10, 2024

Cervical cancer is a significant health problem worldwide, and early detection treatment are critical to improving patient outcomes. To address this challenge, deep learning (DL)-based cervical classification system proposed using 3D convolutional neural network Vision Transformer (ViT) module. The model leverages the capability of CNN extract spatiotemporal features from images employs ViT capture learn complex feature representations. consists an input layer that receives images, followed by convolution block, which extracts images. maps generated down-sampled max-pooling block eliminate redundant information preserve important features. Four models employed efficient different levels abstraction. output each set captures at specific level then supplied into pyramid (FPN) module for concatenation. squeeze-and-excitation (SE) obtain recalibrate responses based on interdependencies between maps, thereby discriminative power model. At last, dimension minimization executed average pooling layer. Its fed kernel extreme machine (KELM) one five classes. KELM uses radial basis function (RBF) mapping in high-dimensional space classifying samples. superiority known simulation results, achieving accuracy 98.6%, demonstrating its potential as effective tool classification. Also, it can be used diagnostic supportive assist medical experts accurately identifying patients.

Language: Английский

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A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: July 15, 2024

Abstract Early diagnosis of abnormal cervical cells enhances the chance prompt treatment for cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems detecting are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, error‐prone. The purpose this study is to present a comprehensive review AI technologies used pre‐cancerous lesions cancer. includes studies where was applied Pap Smear test (cytological test), colposcopy, sociodemographic data other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, positron emission tomography‐scan‐based imaging modalities. We performed searches on Web Science, Medline, Scopus, Inspec. preferred reporting items systematic reviews meta‐analysis guidelines were search, screen, analyze articles. primary search resulted in identifying 9745 followed strict inclusion exclusion criteria, which include windows last decade, journal articles, machine/deep learning‐based methods. A total 58 have been included further analysis after identification, screening, eligibility evaluation. Our shows that deep learning models techniques, whereas machine data. convolutional neural network‐based features yielded representative characteristics CrC. also highlights need generating new easily accessible diverse datasets develop versatile CrC detection. model explainability uncertainty quantification increase trust clinicians stakeholders decision‐making automated detection models. suggests privacy concerns adaptability crucial deployment hence, federated meta‐learning should explored. This article categorized under: Fundamental Concepts Data Knowledge > Explainable Technologies Machine Learning Classification

Language: Английский

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Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study

International Journal of Reproductive BioMedicine (IJRM), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 21, 2023

Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary discover hub gene, key pathways, and exact underlying mechanisms involved developing this disease. Objective: This study aims use gene expression patterns protein-protein interaction (PPI) network analysis identify pathways druggable genes CC. Materials Methods: In silico analysis, 2 microarray datasets; GSE63514 (104 24 normal samples), GSE9750 (42 samples) were extracted from omnibus differentially expressed between them. Gene ontology Kyoto encyclopedia genomes pathway performed via Enrichr database. STRING 12.0 database CytoHubba plugin Cytoscape 3.9.1 software implemented create analyze PPI network. Finally, screened. Results: Based degree method, 10 known as after screening networks by plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, KIF2C are genes, which 5 (KIF11, TOP2A) druggable. Conclusion: research provides a novel vision for designing therapeutic targets patients with However, these findings should be verified through additional experiments. Key words: Protein interactions, Cervical cancer, Hub expression, DEGs.

Language: Английский

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