Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Informatics in Medicine Unlocked, Год журнала: 2024, Номер 47, С. 101504 - 101504

Опубликована: Янв. 1, 2024

Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence deep learning (DL) techniques. The use layers in neural networks, like object form recognition higher and basic edge identification lower layers, markedly improved quality accuracy image segmentation. Consequently, DL using picture segmentation become commonplace, video analysis, facial recognition, etc. Grasping applications, algorithms, current performance, challenges are for advancing DL-based medical However, there's lack studies delving latest state-of-the-art developments this field. Therefore, survey aimed to thoroughly explore most recent applications encompassing an in-depth analysis various commonly used datasets, pre-processing techniques algorithms. This study also investigated advancement done by analyzing their results experimental details. Finally, discussed future research directions Overall, provides comprehensive insight covering its application domains, model exploration, results, challenges, directions—a valuable resource multidisciplinary studies.

Язык: Английский

---

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

BioMedInformatics, Год журнала: 2024, Номер 4(1), С. 236 - 284

Опубликована: Янв. 18, 2024

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such cancer detection. This literature review synthesizes current research deep techniques applied to lung screening diagnosis. summarizes the state-of-the-art in detection, highlighting key advances, limitations, future directions. We prioritized studies utilizing major public datasets, LIDC, LUNA16, JSRT, provide comprehensive overview of field. focus architectures, including 2D 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) vision transformers (ViT). Across studies, models consistently outperformed traditional machine terms accuracy, sensitivity, specificity detection CT scans. is attributed ability automatically learn discriminative features from images model complex spatial relationships. However, several challenges remain be addressed before can widely deployed clinical practice. These include dependence training data, generalization across integration metadata, interpretability. Overall, demonstrates great potential precision medicine. more required rigorously validate address risks. provides insights both computer scientists clinicians, summarizing progress directions analysis.

Язык: Английский

---

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Journal of Imaging, Год журнала: 2024, Номер 10(4), С. 81 - 81

Опубликована: Март 28, 2024

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or sequence images to recognize content, has been used extensively across industries in recent years. However, the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV potential improve patient monitoring, system efficiencies, while reducing workload. In contrast previous reviews, we focus on end-user CV. First, briefly review categorize other (job enhancement, surveillance automation, augmented reality). We then developments hospital setting, outpatient, community settings. The advances monitoring delirium, pain sedation, deterioration, mechanical ventilation, mobility, surgical applications, quantification workload hospital, for events outside highlighted. To identify opportunities future also completed journey mapping at different levels. Lastly, discuss considerations associated with outline processes algorithm development testing limit expansion healthcare. This comprehensive highlights ideas expanded use

Язык: Английский

---

Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Июнь 26, 2024

Язык: Английский

---

Deep learning for lungs cancer detection: a review

Artificial Intelligence Review, Год журнала: 2024, Номер 57(8)

Опубликована: Июль 8, 2024

Abstract Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating diagnosis classification diseases. In this study, we use variety medical imaging modalities, including X-rays, WSI, CT scans, MRI, thoroughly investigate techniques field classification. This study conducts comprehensive Systematic Literature Review (SLR) using for research, providing overview methodology, cutting-edge developments, quality assessments, customized approaches. It presents data from reputable journals concentrates years 2015–2024. solve difficulty manually identifying selecting abstract features images. includes wide range methods classifying but focuses most popular method, Network (CNN). CNN can achieve maximum accuracy because its multi-layer structure, automatic weights, capacity communicate local weights. Various algorithms shown with performance measures like precision, accuracy, specificity, sensitivity, AUC; consistently shows greatest accuracy. The findings highlight important contributions DCNN improving detection classification, making them an invaluable resource researchers looking gain greater knowledge learning’s function applications.

Язык: Английский

---

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Indonesian Journal of Computer Science, Год журнала: 2024, Номер 13(1)

Опубликована: Фев. 20, 2024

As a result of technological advancements, variety medical diagnostic systems have grown rapidly to support the healthcare sectors. Over past years, there has been considerable interest in utilizing deep learning algorithms for proactive diagnosis multiple diseases. In most cases, Coronavirus (COVID-19) and tuberculosis (TB) are diagnosed through examination pulmonary X-rays. Deep can identify with an almost medical-grade level consistency by extracting lung regions X-ray images. The probability detection is increased when classification applied segmented lungs rather than entire X-ray. main focus this paper execute segmentation from images using deeplabv3plus CNN-based semantic model. other CNN architectures, feature resolution diminishes as network becomes deeper due use sequential convolutions pooling or striding within down-sampling stage. To tackle drawback, incorporates "Atrous Convolution" addition modifying convolutional components backbone. experimental results were: accuracy 97.42%, Jaccard index 93.49%, dice coefficient 96.63%. We also conduct extensive comparison between model benchmark architectures. prove ability achieve precise

Язык: Английский

---

Deep learning on medical image analysis

CAAI Transactions on Intelligence Technology, Год журнала: 2024, Номер unknown

Опубликована: Июнь 24, 2024

Abstract Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features patterns from extensive datasets. The paper covers the structure of CNN its advances explores different types transfer learning strategies well classic pre‐trained models. also discusses how has been applied to areas within medical analysis. This comprehensive overview aims assist researchers, clinicians, policymakers by providing detailed insights, helping them make informed decisions about future research policy initiatives improve patient outcomes.

Язык: Английский

---

An Analysis of Deep Transfer Learning-Based Approaches for Prediction and Prognosis of Multiple Respiratory Diseases Using Pulmonary Images

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 31(2), С. 1023 - 1049

Опубликована: Окт. 31, 2023

Язык: Английский

---

An effective obstacle detection system using deep learning advantages to aid blind and visually impaired navigation

Ain Shams Engineering Journal, Год журнала: 2023, Номер 15(2), С. 102387 - 102387

Опубликована: Июль 16, 2023

Blind and visually impaired people face different challenges when navigating indoors outdoors. In this context, we suggest developing an obstacle detection system based on a modified YOLO v5 neural network architecture. The suggested is capable of recognizing locating set landmark indoor outdoor objects that are extremely useful for Visually Impaired (BVI) navigation aids. Training evaluation experiments were conducted using two datasets: the IODR dataset object MS COCO detection. We used several optimization strategies, such as model width scaling, quantization, channel pruning, to guarantee work implemented in embedded devices lightweight manner. proposed was successful achieving results competitive terms processing time well precision

Язык: Английский

---

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Journal of Personalized Medicine, Год журнала: 2023, Номер 13(12), С. 1703 - 1703

Опубликована: Дек. 12, 2023

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving technology. However, using machine in poses challenges related availability, such as incomplete, unstructured, fragmented data, well issues privacy, security, format standardization. Furthermore, there is a risk of bias discrimination models. Thus, developing an accurate prediction model from scratch can be expensive complicated task that often requires extensive experiments complex computations. Transfer methods emerged feasible solution address these by transferring knowledge previously trained develop high-performance models for new task. This survey paper provides comprehensive study the effectiveness transfer applications enhance accuracy efficiency diagnoses prognoses, improve healthcare services. The first part this presents discusses most common technologies valuable resources applications, including learning. second meaning learning, clarifying categories types transfer. It also explains strategies, their role addressing models, specifically on data. These include feature extraction, fine-tuning, domain adaptation, multitask federated few-/single-/zero-shot highlights key features each method strategy, limitations applications. Overall, which aims inspire researchers gain approaches health, current strategies overcome limitations, apply them variety technologies.

Язык: Английский

---