Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri

Kaya, Umut; Yilmaz, Atınç; Dikmen, Yalım

doi:10.31590/ejosat.573248

Cited by 25 publications

(12 citation statements)

References 83 publications

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“…The architectures created through suitable methods for deep learning are utilized in cancer diagnosis, gene selection and classification, gene variety, drug design, compound protein interaction, RNA-protein relationship, and bioinformatics applications such as DNA methylation [32].…”

Section: Deep Learning Architecturementioning

confidence: 99%

Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

Süyun¹,

Taşdemir²,

Bilis³

et al. 2021

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Range throughout Turkey in this paper, the author trained the continuous neural networks, and used a total of 4,000 fundus images, including images with different degrees of fundus disorders and images without disorders, so that CNN can detect whether the patient has hypertension and arteriosclerosis according to macular degeneration in the fundus images. In order to obtain more effective results from the deep learning structure using convolutional neural network, this paper prepared more data sets on the basis of Turkey, combined with the local data sets to educate the deep learning model, so as to integrate the data globally, which can help standardize the results and improve the accuracy. The system is used to diagnose retinal vascular degeneration, such as fundus vascular disease and macular edema disease. Based on this basic understanding, the research has been used for the detection and classification of hypertensive retinopathy that has similar causes. The author also points out the limitations of the system. Among them, the most important limitation is the need for long-term financial sustainability.

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Section: Deep Learning Architecturementioning

confidence: 99%

Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

Süyun¹,

Taşdemir²,

Bilis³

et al. 2021

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“…Thus, the hidden layer of the previous network acts as the visible (input) layer of the next network. These two layers have non-directional connections, which allows DBN to be used in supervised and unsupervised learning practices [46,47]. In the DBN, the greedy layer-by-layer training method is used to learn the most efficient weights from top to bottom [48].…”

Section: Deep Belief Network (Dbns)mentioning

confidence: 99%

Deep learning techniques of losses in data transmitted in wireless sensor networks

Ersoy¹,

Aksoy²

2021

Turk J Elec Eng & Comp Sci

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Wireless sensor network systems are frequently used today along with the rapidly developing technology. Wireless sensor networks, which form the basis of the Internet of Things, have a wide user base in the world. For example, wireless sensor networks are used in many areas from education to health, from military applications to home applications. It enables the data obtained from the sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasing number of nodes in WSN, data traffic density also increases. Due to the limitations of the WSN network, lost packet rates also increase with increasing data traffic. In this study, a data set was created by examining the data transfers of different amounts of WSN nodes placed in different places. The effects of the number of sensors and the distance between them were obtained from the data set. In this study, a data set was created by collecting the data from the sensor nodes placed at 1500m x 1500m intervals in the ns-3 discrete event emulator program. Today, with the rapid development of technology, deep learning methods which are one of the artificial intelligence methods, are also used in WSN. In this study, the loss rate in the transferred data packets was tried to be estimated with the highest accuracy by using Deep Belief Network (DBN), Recurrent Neural Network (RNN) and Deep Neural Network (DNN) deep learning techniques over the obtained dataset. Of these three deep learning methods, DNN deep learning method was found to accurately estimate the loss rate in the transferred data packets with an accuracy rate of 88.50%.

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“…Cancer has many forms like skin cancer, breast cancer, leukaemia, prostate cancer, brain cancer etc. In 2018, approximately 9.6 million people suffered and died from cancer [3]. Figs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

Kumar¹,

Alqahtani²

2022

Computer Modeling in Engineering &Amp; Sciences

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Cancer is one of the most critical diseases that has caused several deaths in today's world. In most cases, doctors and practitioners are only able to diagnose cancer in its later stages. In the later stages, planning cancer treatment and increasing the patient's survival rate becomes a very challenging task. Therefore, it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning. Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease. However, manual interpretation of medical images is costly, time-consuming and biased. Nowadays, deep learning, a subset of artificial intelligence, is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention. Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features. This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis, mainly focusing on breast cancer, brain cancer, skin cancer, and prostate cancer. This study describes various deep learning models and steps for applying deep learning models in detecting cancer. Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages. Based on the identified challenges, we provide a few promising future research directions for fellow researchers in the field. The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

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Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri

Cited by 25 publications

References 83 publications

Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

Deep learning techniques of losses in data transmitted in wireless sensor networks

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

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