Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh, Maha; Almahmoud, Mothanna; Ali, Mustafa N.; Al-Badarneh, Amer; El-Heis, Mwaffaq; Abualigah, Laith; Altalhi, Maryam; Alaiad, Ahmad; Gandomi, Amir H.

doi:10.3390/bdcc6010002

Cited by 22 publications

(7 citation statements)

References 70 publications

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“…The detection of AD using DL was performed by [23], who used a dataset of digital subtracted angiogram scans of AD subjects. The feature extraction was made using pre-trained models-i.e., InceptionV3 and DenseNet201.…”

Section: Previous Workmentioning

confidence: 99%

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Ullah¹,

Jamjoom²

2023

Computers, Materials &Amp; Continua

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Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD's progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such as deep learning applied to brain images obtained with these tools, has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD. In this study, we proposed an intelligent methodology for building a convolutional neural network (CNN) from scratch to detect AD stages from the brain MRI images dataset and to improve patient care. It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems. Therefore, for better understanding of classifiers and to overcome the model overfitting problem, we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset. All experiments were conducted using Alzheimer's MRI dataset consisting of brain MRI scanned images. The performance of the proposed model determines detection of the four stages of AD. Experimental results show high performance of the proposed model in that the model achieved a 99.38% accuracy rate, which is the highest so far. Moreover, the proposed model performance in terms of accuracy, precision, sensitivity, specificity, and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

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Section: Previous Workmentioning

confidence: 99%

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Ullah¹,

Jamjoom²

2023

Computers, Materials &Amp; Continua

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“…Figure 1 shows a case of KT, a renal mass lesion in the left kidney measuring about 4 cm besides 3D volume rendering of the kidneys (kidney in pink and renal cancer in blue). A tumor threatens a person's life, so many procedures resolve this obstacle through accurate tumor diagnosis [8,9].…”

Section: Introductionmentioning

confidence: 99%

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Alzu’bi

Abdullah

Hmeidi

et al. 2022

Journal of Healthcare Engineering

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Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist’s workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease’s track and preserve the patient’s life.

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“…Artificial intelligence (AI) has been shown to have incredible benefits in health care. The recent advances in hardware, big data technology, and applications were the primary factors in making it more effective to devise techniques for automating medical processes [1,2]. Kidney disease identification is a multidisciplinary area that information technology experts are investigating to assist medical personnel by modeling the biological processes of the human kidneys and producing reliable diagnostic results.…”

Section: Introductionmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

Alzu’bi

Abdullah

et al. 2022

BDCC

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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

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Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Cited by 22 publications

References 70 publications

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

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