Review of Machine Learning Techniques in Health Care

Pillai, Rohan; Oza, Parita; Sharma, Priyanka

doi:10.1007/978-3-030-29407-6_9

Cited by 24 publications

(8 citation statements)

References 9 publications

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“…most important algorithms for different data, biological data can be classified into images, signals, and sequences; this classification can be graphically learned in Figure 3. Standard algorithms, such as support vector machines (SVMs), Linear classifiers, or random forests have achieved interesting results over the years; the authors of [110]- [112] explore the literature on these algorithms. They can still be used, but we do not explore them in depth because they require manual extraction of characteristics, which is time-consuming and needs domain-specific know-how.…”

Section: B Artificial Intelligence For Ehealth Datamentioning

confidence: 99%

The Next Generation of eHealth: A Multidisciplinary Survey

et al. 2022

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Over the past two years, the spread of COVID-19 has spurred the use of information and communication technologies (ICT) in aid of healthcare. The need to guarantee continuity to care has promoted research and industry activities aimed at developing solutions for the digitalization of the procedures to be performed to provide health services, even in emergency scenarios. Digital collection, transmission, and processing of health data represent the starting point for fulfilling this innovation process but also bring heterogeneous challenges. These motivations led to the elaboration of this work, which analyzes innovative and technological tools for the development of digital health (eHealth) through the collection of multisectoral literature, produced thanks to the cooperation of varied research groups, thus providing a multidisciplinary survey. Since digital health is expected to be one of the leading applications of the sixth-generation (6G) wireless cellular networks, this paper covers the related telecommunications aspects. Furthermore, the exploitation of artificial intelligence paradigms to elaborate massive amounts of biological data is examined. Given the extreme sensitivity of health data, this paper also investigates security and privacy issues. In particular, the main techniques and approaches to guarantee security properties (i.e., anonymity, responsibility, authentication, confidentiality, integrity, non-repudiation, and revocability) are studied. Applications involving innovative electromagnetic systems for healthcare and assisted living services are described to provide an example of an eHealth scenario leveraging ICT. Finally, the telemedicine-related regulations of the European Commission are analyzed, with particular reference to the General Data Protection Regulation (GDPR).

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Section: B Artificial Intelligence For Ehealth Datamentioning

confidence: 99%

The Next Generation of eHealth: A Multidisciplinary Survey

et al. 2022

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“…CNNs have the capabilities to automatically learn features through several network layers from a large set of labelled datasets [ 1 ]. Concerning the biomedical image analysis topic, CNNs have been successfully utilised for various tasks such as lesion or tumour classification, suspicious region detection, and abnormality detection [ 2 , 3 , 4 ]. DL-based solutions serve as a second opinion tool for expert radiologists and assist them in decision-making, and proper treatment planning [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

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“…A comprehensive study of mammogram classification techniques of various deep learning and machine learning approaches is presented in [11]. Apart from these, Support vector machine (SVM), naive bayes , artificial neural network (ANN), and set classifiers [12] are some of the machine learning algorithms that have proven popular for the development of computer-aided diagnosis systems for breast cancer [13,14]. Another work by Ikechukwu et al [19] presented a comparative study of two pre-trained models, such as ResNet-50 and VGG-19, against training a model from scratch (Iyke-Net).…”

Section: Introductionmentioning

confidence: 99%

A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

Oza¹,

Sharma²,

Patel³

2022

SCPE

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Breast cancer is a deadly disease that affects the lives of millions of women throughout the world. Over time, the number of cases of breast cancer has increased. Preventing this disease is difficult and remains unidentified, but the survival percentage can be improved if diagnosed early. The progress of computer-assisted diagnosis (CAD) of breast cancer has seen a lot of improvements thanks to advances in deep learning. With the notable advancement of deep neural networks, diagnostic capabilities are nearing a human expert's. In this paper, we used EfficientNet to classify mammograms. This model is introduced with the new concept of model scaling called compound scaling. Compound scaling is the strategy which scales the model by adding more layers to extend the receptive field along with more channels to catch the detailed features of larger input. We also compare the performance of various variants of EfficientNet over CBIS-DDSM mammogram datasets. We used the optimum fine-tuning procedure to represent the importance of transfer learning (TL) during training.

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Review of Machine Learning Techniques in Health Care

Cited by 24 publications

References 9 publications

The Next Generation of eHealth: A Multidisciplinary Survey

The Next Generation of eHealth: A Multidisciplinary Survey

Image Augmentation Techniques for Mammogram Analysis

A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

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