A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4178 - 4178

Published: April 22, 2023

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic treatment abilities by utilizing applications in the healthcare domain. data used many researchers detect diseases identify patterns. In current literature, there very few studies that address algorithms improve accuracy efficiency. We examined effectiveness of improving time series metrics for heart rate transmission (accuracy efficiency). this paper, we reviewed several applications. After a comprehensive overview investigation supervised unsupervised algorithms, also demonstrated tasks based on past values (along with reviewing their feasibility both small large datasets).

Language: Английский

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A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 90, P. 105858 - 105858

Published: Dec. 22, 2023

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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Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

AIMS Public Health, Journal Year: 2024, Volume and Issue: 11(1), P. 58 - 109

Published: Jan. 1, 2024

<abstract> <p>In recent years, machine learning (ML) and deep (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given current progress fields of ML DL, there exists promising potential for both provide support realm healthcare. This study offered an exhaustive survey on DL system, concentrating vital state art features, integration benefits, applications, prospects future guidelines. To conduct research, we found most prominent journal conference databases using distinct keywords discover scholarly consequences. First, furnished along with cutting-edge ML-DL-based analysis smart a compendious manner. Next, integrated advancement services including ML-healthcare, DL-healthcare, ML-DL-healthcare. We then DL-based applications industry. Eventually, emphasized research disputes recommendations further studies based our observations.</p> </abstract>

Language: Английский

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Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 8, 2024

Background Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over last few decades. Early and accurate detection this type can result better prognoses less invasive treatments for patients. With advances Artificial Intelligence (AI), tools have emerged that facilitate diagnosis classify dermatological images, complementing traditional clinical assessments being applicable where there shortage specialists. Its adoption requires analysis efficacy, safety, ethical considerations, as well considering genetic ethnic diversity Objective The systematic review aims to examine research on detection, classification, assessment skin images settings. Methods We conducted literature search PubMed, Scopus, Embase, Web Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, critical appraisal were carried out by two independent reviewers. Results subsequently presented through narrative synthesis. Through search, 760 identified four databases, from which only 18 selected, focusing developing, implementing, validating systems detect, diagnose, This covers descriptive analysis, scenarios, processing techniques, study results perspectives, physician diversity, accessibility, participation. Conclusion application artificial intelligence dermatology has potential revolutionize early cancer. However, it imperative validate collaborate healthcare professionals ensure its effectiveness safety.

Language: Английский

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A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 21, 2024

Abstract Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin by utilizing Convolution Neural Network architecture optimizing hyperparameters. The proposed aims increase the precision efficacy of recognition consequently enhance patients' experiences. investigation tackle various significant challenges recognition, encompassing feature extraction, model design, utilizes advanced deep-learning methodologies extract complex features patterns from images. We learning procedure deep integrating Standard U-Net Improved MobileNet-V3 with optimization techniques, allowing differentiate malignant benign cancers. Also substituted crossed-entropy loss function Mobilenet-v3 mathematical framework bias accuracy. model's squeeze excitation component was replaced practical channel attention achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been leverage synthetic effectively. dilated convolutions were incorporated into receptive field. hyperparameters utmost importance improving efficiency models. To fine-tune hyperparameter, we employ sophisticated methods such as Bayesian method using pre-trained CNN MobileNet-V3. compared existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 VGG-19 on “HAM-10000 Melanoma Cancer dataset". empirical findings illustrate optimized hybrid outperforms detection segmentation techniques based high 97.84%, sensitivity 96.35%, accuracy 98.86% specificity 97.32%. enhanced performance this research resulted timelier more diagnoses, potentially contributing life-saving outcomes mitigating healthcare expenditures.

Language: Английский

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A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 10

Published: April 5, 2023

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which anywhere from 1 to 15% global population and thus; considered one leading causes chronic (CKD). In addition renal cancer is tenth most prevalent type cancer, accounting for 2.5% all cancers. Artificial intelligence (AI) in medical systems can assist radiologists other healthcare professionals diagnosing different (RD) with high reliability. This study proposes an AI-based transfer learning framework detect RD at early stage. The presented on CT scans images microscopic histopathological examinations will help automatically accurately classify patients using convolutional neural network (CNN), pre-trained models, optimization algorithm images. used CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, NASNetMobile. addition, Sparrow search (SpaSA) enhance model's performance best configuration. Two datasets were used, first dataset four classes: cyst, normal, stone, tumor. case latter, there five categories within second relate severity tumor: Grade 0, 1, 2, 3, 4. DenseNet201 MobileNet four-classes compared others. Besides, SGD Nesterov parameters optimizer recommended by three while two only recommend AdaGrad AdaMax. five-class dataset, Xception best. Experimental results prove superiority proposed over state-of-the-art classification models. records accuracy 99.98% (four classes) 100% (five classes).

Language: Английский

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A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Language: Английский

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A hybrid deep learning skin cancer prediction framework

Engineering Science and Technology an International Journal, Journal Year: 2024, Volume and Issue: 57, P. 101818 - 101818

Published: Aug. 27, 2024

Language: Английский

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Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 454 - 454

Published: Feb. 19, 2024

In recent years, there has been growing interest in the use of computer-assisted technology for early detection skin cancer through analysis dermatoscopic images. However, accuracy illustrated behind state-of-the-art approaches depends on several factors, such as quality images and interpretation results by medical experts. This systematic review aims to critically assess efficacy challenges this research field order explain usability limitations highlight potential future lines work scientific clinical community. study, was carried out over 45 contemporary studies extracted from databases Web Science Scopus. Several computer vision techniques related image video processing diagnosis were identified. context, focus process included algorithms employed, result accuracy, validation metrics. Thus, yielded significant advancements using deep learning machine algorithms. Lastly, establishes a foundation research, highlighting contributions opportunities improve effectiveness learning.

Language: Английский

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