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

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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

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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

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Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Diagnostics, Год журнала: 2023, Номер 13(6), С. 1063 - 1063

Опубликована: Март 10, 2023

Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming laborious, computer technology now being introduced to aid in diagnosis. Due a large amount noise blurred lesion edges osteosarcoma MRI images, high-precision segmentation methods require computational resources difficult use developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, threshold filter (TSF) was used pre-screen the redundant data. Then, fast NLM algorithm for denoising. Finally, method enhancement (TBNet) designed segment pre-processed fusing Transformer based on UNet network. TBNet skip-free connected U-Net includes channel-edge cross-fusion transformer combined loss function. This solution optimizes diagnostic efficiency solves problem edges, providing more help reference doctors diagnose osteosarcoma. results than 4000 show that our proposed has good effect performance, Dice Similarity Coefficient (DSC) reaching 0.949, other evaluation indexes such as Intersection Union (IOU) recall better methods.

Язык: Английский

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Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июль 5, 2024

Язык: Английский

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Vision Transformer Based Effective Model for Early Detection and Classification of Lung Cancer

SN Computer Science, Год журнала: 2024, Номер 5(7)

Опубликована: Авг. 29, 2024

Язык: Английский

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RETRACTED ARTICLE: Lung cancer CT image classification using hybrid-SVM transfer learning approach

Soft Computing, Год журнала: 2023, Номер 27(14), С. 9845 - 9859

Опубликована: Май 26, 2023

Язык: Английский

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Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Ноя. 23, 2023

Abstract Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% all new diagnoses and a 30–55% recurrence rate after surgery. Thus, an accurate prediction risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment patients. The radiomic analysis CT images has already shown great potential solving this task; specifically, Convolutional Neural Networks (CNNs) have been proposed providing good performances. Recently, Vision Transformers (ViTs) introduced, reaching comparable even better performances than traditional CNNs image classification. aim paper was compare different state-of-the-art deep learning algorithms predict In work, using public database 144 patients, we implemented transfer approach, involving architectures like pre-trained ViTs, Pyramid Transformers, Swin from images, comparing their CNNs. Although, best study are reached via AUC, Accuracy, Sensitivity, Specificity, Precision equal 0.91, 0.89, 0.85, 0.90, 0.78, respectively, Transformer reach ones 0.86, 0.81, 0.75, respectively. Based on our preliminary experimental results, it appears that do not add improvements terms predictive performance addressed problem.

Язык: Английский

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A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Applied Sciences, Год журнала: 2024, Номер 14(11), С. 4428 - 4428

Опубликована: Май 23, 2024

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage retinal blood vessels. Initial detection prompt medical intervention are vital preventing progressive impairment. Today’s growing field presents a more significant workload diagnostic demands on professionals. In proposed study, convolutional neural network (CNN) employed detect stages of DR. This research crucial for studying DR because its innovative methodology incorporating two different public datasets. strategy enhances model’s capacity generalize unseen images, as each dataset encompasses unique demographics clinical circumstances. The learn capture complicated hierarchical image features with asymmetric weights. Each preprocessed using contrast-limited adaptive histogram equalization discrete wavelet transform. model trained validated combined datasets Dataset Retinopathy Asia-Pacific Tele-Ophthalmology Society. CNN tuned learning rates optimizers. An accuracy 72% an area under curve score 0.90 was achieved by Adam optimizer. recommended study results may reduce diabetes-related early identification severity.

Язык: Английский

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An improved RIME optimization algorithm for lung cancer image segmentation

Computers in Biology and Medicine, Год журнала: 2024, Номер 174, С. 108219 - 108219

Опубликована: Март 11, 2024

Язык: Английский

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