Identification of Alzheimer Disease by Using Hybrid Deep Models

Zia, Muhammad Azam; Saeed, Zeeshan; Asghar, Naeem; Majeed, Bushra; Hashim, Muhammad

doi:10.1007/978-3-031-10388-9_34

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…A DCNN model has been created in the suggested work. Convolution, pooling, flattening, and dense layers are all present [9]. CNN uses a fully connected layer to extract the features automatically from the input image and identify them as either fracture or nonfracture bone.…”

Section: Proposed Modelmentioning

confidence: 99%

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

et al. 2023

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Human bones are the hard organs that protect vital organs such as the heart, lungs, and other internal organs. Fractures of the bones are a prevalent issue among humans. Bone fractures may develop from an accident or another circumstance when there is great pressure on the bones. It may be difficult and time-consuming to determine the site of a fracture in a patient who is suffering discomfort. The manual examination of fractures during radiological interpretation is a time-consuming and error-prone process. This may result in erroneous detection, poor fracture healing, and an extensive procedure. So, this research proposed an effective approach to rectifying bone fractures with the inclusion of the latest technologies. The solution is proposed by employing a Deep learning model. Moreover, a novel concept of classification is also incorporated. Firstly; the MURA dataset was collected from Stanford. Secondly; The proposed model used techniques like DCNN (Deep Convolution Neural Network) and use Alex Net model. Bones are classified into fractured or non-fractured through a classification approach. The proposed model was created using Google Colab. The proposed model was trained by repeating several experiments. The performance was evaluated based on accuracy. The suggested model results were compared with baseline algorithms as well. Consequently, the findings of this work will be useful for the medical industry.

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Section: Proposed Modelmentioning

confidence: 99%

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

et al. 2023

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Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Shi,

Feng,

Zhi

et al. 2024

MedComm

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Cognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far‐reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative‐induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular‐induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high‐dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning‐based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use.

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Enhanced Alzheimer’s Disease Classification Using Multilayer Deep Convolutional Neural Network-Based Experimentations

Kumar¹,

Sasikala²

2023

Iran J Sci Technol Trans Electr Eng

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Identification of Alzheimer Disease by Using Hybrid Deep Models

Cited by 3 publications

References 13 publications

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

Machine learning‐based radiomics in neurodegenerative and cerebrovascular disease

Enhanced Alzheimer’s Disease Classification Using Multilayer Deep Convolutional Neural Network-Based Experimentations

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