Computer-Aided Diagnosis System for Early Prediction of Atherosclerosis using Machine Learning and K-fold cross-validation

Cherradi, Bouchaib; Terrada, Oumaima; Ouhmida, Asmae; Hamida, Soufiane; Raihani, Abdelhadi; Bouattane, Omar

doi:10.1109/icoten52080.2021.9493524

Cited by 36 publications

(14 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, although these techniques give excellent results, compared to traditional methods, their disadvantage is that they are computationally expensive and require a great effort for training. Keyphrases are rare in some documents such as biomedical documents [11]- [13], which is reflected in the performance of methods using embedding techniques. Therefore, it is necessary to generate keyphrases instead of extracting them into biomedical documents.…”

Section: Sentence Embedding Techniquesmentioning

confidence: 99%

Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

View full text Add to dashboard Cite

The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.

show abstract

Section: Sentence Embedding Techniquesmentioning

confidence: 99%

Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…In fact, various human diseases could be detected using such techniques, including COVID-19 [15]- [19], Parkinson's disease [20], breast cancer [21], diabetes diseases [22], medical image segmentation [23], and heart disease prediction [24]- [26]. A vast range of different scientific topics has developed because of advances in AI [27]- [35].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Lamrani¹,

Cherradi²,

Gannour³

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors.

show abstract

“…The set of digital technology developments has favored the coordination of large-scale operations like prediction and early diagnosis [ [5] , [6] , [7] , [8] , [9] , [10] ], population-level mass screening [ 11 , 12 ], quick contact tracing [ 13 , 14 ], vaccination and pharmaceutical supply chain management [ 15 ], telemedicine consultations [ 16 ], digital training [ 17 ] and e-commerce expansion [ 18 ]. Otherwise, digital technologies such as machine learning (ML) and deep learning (DL) has proved its efficiency in many computer-aided diagnosis systems for different disease [ [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study

Bennacer

Aaroud

Sabiri

et al. 2022

Informatics in Medicine Unlocked

View full text Add to dashboard Cite

Computer-Aided Diagnosis System for Early Prediction of Atherosclerosis using Machine Learning and K-fold cross-validation

Cited by 36 publications

References 39 publications

Present and absent keyphrases extraction: an approach based on sentence embedding

Present and absent keyphrases extraction: an approach based on sentence embedding

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study

Contact Info

Product

Resources

About