A Transfer Learning Approach for Predicting Alzheimer's Disease

Rajeswari, Sreeja Sasidharan; Nair, Manjusha

doi:10.1109/icnte51185.2021.9487746

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…Transfer öğrenmenin yapılacak olan tahmin işlevinin öğrenme performansını iyileştirmesi amaçlamaktadır [19]. Transfer öğrenme yönteminde, önceden eğitilmiş bir model, büyük ve karmaşık verileri eğitmek için belli katmanları dondurularak yeniden kullanılmaktadır [36].…”

Section: Transfer öğRenmeunclassified

Evrişimsel Sinir Ağları ile Görüntülerde Gürültü Türünü Saptama

Güneş,

Çetin Kaya

2024

Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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Gürültü, görüntü elde etme sırasında görüntüye eklenen istenmeyen sinyallerdir. Bir görüntüden gürültünün arındırılmasında kullanılan filtre yöntemlerinin başarılı olabilmesi için gürültü türünün doğru şekilde analiz edilmesi gerekmektedir. Bu çalışma ile görüntülerdeki gürültü türünün ve gürültüsüz görüntülerin doğru ve pratik şekilde saptanması hedeflenmiştir. Ayrıca, Evrişimli Sinir Ağları (ESA) ile gürültü tahmininde hangi eniyileme algoritmasının tercih edilebileceğine ışık tutulmaya çalışılmıştır. Görüntülerde tuz-biber, gauss ve benek gürültü türlerinin saptanması için VGG-16 mimarisi temel alınarak bir ESA modeli önerilmiştir. Önerilen model transfer öğrenme yöntemi ve ince ayar yaklaşımı kullanılarak eğitilmiş ve beş eniyileme algoritmasının model başarımı üzerindeki etkisi incelenmiştir. Gürültü türünün saptanması için en iyi doğruluk %98,75 ile RMSProp eniyileme algoritması kullanılarak elde edilmiştir. Başarım performansları, gürültü türünün saptanmasında önerilen ESA mimarisinin başarı ile kullanılabileceği gösterilmiştir.

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Section: Transfer öğRenmeunclassified

Evrişimsel Sinir Ağları ile Görüntülerde Gürültü Türünü Saptama

Güneş,

Çetin Kaya

2024

Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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“…Currently, neurologists, neuroradiologists, and researchers can more accurately diagnose AD in a patient while they are still alive [20]. There are different techniques for diagnosing AD [21], as follows: (1) 2) [22].…”

Section: Clinical Techniques To Detect Admentioning

confidence: 99%

“…Alzheimer's disease (AD) is the most frequent cause of dementia among the elderly. AD is a neurologic disorder that affects patient memory and cognitive skills [1]. Based on the 2022 World Alzheimer Report, AD is among the 7 major causes of death globally and 12.7 million people ≥ 65 years of age will be diagnosed with AD by 2050 [2].…”

Section: Introductionmentioning

confidence: 99%

“…DA refers specifically to a learning technique that aims to improve model performance on a target domain by using the knowledge learned from a source domain. DA has drawn increasing attention as a potential solution for the domain shift issue for medical image datasets because DA attempts to narrow the performance gap between different, but related domains [1,6].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

Bakheet

Huang

et al. 2023

Radiology Science

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Alzheimer’s disease (AD) is a type of brain disease that affects a person’s ability to perform daily tasks. Modern neuroimaging techniques have made it possible to detect structural and functional changes in the brain that are linked to AD, and machine learning (ML)-based methods have been extensively developed to help physicians achieve fast and accurate imaging-based AD detection. One critical issue when deploying ML methods in clinical applications is the domain shift that exists between the training and test data, which may significantly attenuate a model’s performance. To resolve this issue, domain adaptation (DA) is needed to narrow the performance gap between data from domains with different distributions. The purpose of this review is to offer insight into the state of ML and DA research in the field of neuroimaging-based AD detection. The limitations of existing studies, as well as opportunities for future studies, are discussed with the hope that more investigations will be conducted in the future to optimize the clinical workflow for AD diagnosis and treatment.

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“…The model achieved an accuracy of 91.01% (mAP). Rajesware et al [18], used transfer learning methods to detect Alzheimer's disease, which is very difficult to identify and diagnose. In this context, they used techniques such as VGG-19, VGG-16, ResNet50, and Xception.…”

Section: Introductionmentioning

confidence: 99%

Yolov3 Tabanlı Atık Tespit Sistemleri

KARACA,

UZUN,

KAÇAR

2023

Journal of Smart Systems Research

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Today, the increase in the number of people, advances in industry and technology cause an increase in the number of wastes generated with the acceleration of production. It is important for the future of our country and the world that these wastes are more easily identified and recycled. In the process of recycling wastes, the classification of wastes as well as their collection requires costly energy and manpower. Wastes are basically separated into paper, plastic, glass and metal. Various studies have been carried out to complete these processes in a shorter and easier way with technologies such as artificial intelligence, deep learning and image processing. In this study, a dataset of paper, plastic and food and beverage wastes that are common in the environment was created. In this dataset, paper cups, plastic water bottles and fast food wastes were detected from different locations in nature and photographed. These images were labeled and trained and tested with YoloV3 in deep learning algorithms. In addition, in order to compare the performance of the new dataset, studies were conducted on a global dataset used in the literature. As a result of the studies, it was observed that it was successful in classifying the newly created dataset and the global dataset.

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A Transfer Learning Approach for Predicting Alzheimer's Disease

Cited by 11 publications

References 16 publications

Evrişimsel Sinir Ağları ile Görüntülerde Gürültü Türünü Saptama

Evrişimsel Sinir Ağları ile Görüntülerde Gürültü Türünü Saptama

Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

Yolov3 Tabanlı Atık Tespit Sistemleri

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