Deep Learning in Healthcare

Kaul, Deeksha; Raju, Harika; Tripathy, B. K.

doi:10.1007/978-3-030-75855-4_6

Cited by 42 publications

(10 citation statements)

References 35 publications

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“…Deep learning, which is based on a machine learning approach, deals with complex input-output mappings. Deep neural networks are a popular technique for processing and analyzing medical data because of their reliability and resemblance to how human functions [15]. With little manual engineering required, Computer models are enabled by deep learning that is made up of many processing layers to discover data representations at various levels of abstraction [16].…”

Section: Deep Learning Methodologiesmentioning

confidence: 99%

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

Javed¹,

Khan

2022

JCBI

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The processing of medical data has profited from automation and contemporary computing breakthroughs, which have given rise to several novel instructional approaches. In both traditional and cutting-edge disciplines, deep learning has emerged as a cutting-edge machine learning paradigm. Deep learning algorithms have developed into supervised, semi-supervised, and unsupervised modes for a variety of real-time applications. The technology has shown to be broadly applicable in image processing, computer vision, medical diagnostics, robotics, and control operation. Deep learning is essential in the field of medical science for recognizing various diseases and solving health issues. The deep learning revolution will significantly alter cardiovascular imaging within the next decade. To ensure that deep learning may meaningfully impact clinical practice, it is crucial for medical practitioners to stay up with these breakthroughs. This evaluation is intended to be a preliminary step in that process. In this study cardiovascular disease diagnosis and classification have been examined using different state-of-the-art deep learning approaches. Deep Neural Networks can be used to improve the classification of heart disease as a whole in a crucial field called heart illness. The classification process can be carried out using a variety of methods, such as DL, KNN, SVM, ANN Nave Bayes, Random Forest, SSD, DNN, and TDNN where DL exhibited maximum accuracy. Although deep learning-based automated cardiovascular classification algorithms have shown highly accurate results, they have not yet been widely used by healthcare professionals.

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Section: Deep Learning Methodologiesmentioning

confidence: 99%

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

Javed¹,

Khan

2022

JCBI

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“…Thus, early diagnosis of this disease with computer-assisted tools is one of the most interesting and very important research interests for medical and computer science [9]. [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Implementation using Convolutional Neural Network for Alzheimer’s Classification

Priyatama

Sari

Azhar

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Alzheimer's disease is the most common cause of dementia. Dementia refers to brain symptoms such as memory loss, difficulty thinking and problem solving and even speaking. This stage of development of neuropsychiatric symptoms is usually examined using magnetic resonance images (MRI) of the brain. The detection of Alzheimer's disease from data such as MRI using machine learning has been the subject of research in recent years. This technology has facilitated the work of medical experts and accelerated the medical process. In this study we target the classification of Alzheimer's disease images using convolutional neural network (CNN) and transfer learning (VGG16 and VGG19). The objective of this study is to classify Alzheimer's disease images into four classes that are recognized by medical experts and the results of this study are several evaluation metrics. Through experiments conducted on the dataset, this research has proven that the algorithm used is able to classify MRI of Alzheimer's disease into four classes known to medical experts. The accuracy of the first CNN model is 75.01%, the second VGG16 model is 80.10% and the third VGG19 model is 80.28%.

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“…Healthcare is one of the domains in which ML is expected to provide substantial improvements in the delivery of patient care worldwide (WHO, 2021). Given the rapid growth in the number of models over the last couple of years (Ravì et al, 2016;Miotto et al, 2018;Kaul et al, 2022;Javaid et al, 2022), healthcare applications deserve special consideration considering the sensitive nature of the data that is required to train the models and the safety-critical nature of medical decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we focus on state-of-the-art (SOTA) literature published in the last three years, considering the high proliferation of ML in healthcare and recent methodological advancements in ML and DL (e.g., network architectures, model pre-training, etc.). Third, we consider studies that develop or apply methodologies using two popular modalities based on publicly available datasets and stateof-the-art in ML for healthcare, namely medical images and data extracted from Electronic Health Records (EHR) (Kaul et al, 2022).Despite the use of other input modalities in medical applications, such as video (Ouyang et al, 2020) or text (Srivastava et al, 2019), our review exclusively focuses on medical images and EHR as they are the most prevalent input modalities in diagnostic and prognostic settings (Shehab et al, 2022). Lastly, although we acknowledge the importance of security for ML models, it is out of the scope of this paper since we primarily focus on privacy.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Guerra-Manzanares¹,

Lopez²,

Maniatakos³

et al. 2023

Preprint

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Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.

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Deep Learning in Healthcare

Cited by 42 publications

References 35 publications

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

A Systematic Analysis for Cardiovascular Disease Classification Using Deep Learning

Deep Learning Implementation using Convolutional Neural Network for Alzheimer’s Classification

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

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