Article
Mathematics, Interdisciplinary Applications
Mohd Anul Haq, Ilyas Khan, Ahsan Ahmed, Sayed M. Eldin, Ali Alshehri, Nivin A. Ghamry
Summary: In this paper, a novel Deep Convolutional Neural Network for Brain Tumor (DCNNBT) is proposed for the detection and classification of brain tumors, emphasizing the importance of early diagnosis for effective treatment and improved patient survival rates.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2023)
Article
Biology
Ramin Ranjbarzadeh, Annalina Caputo, Erfan Babaee Tirkolaee, Saeid Jafarzadeh Ghoushchi, Malika Bendechache
Summary: This study reviews recent Artificial Intelligence (AI) methods for diagnosing brain tumors using MRI images. MRI has become a widely used noninvasive imaging technique in the diagnosis and segmentation of brain tumors. However, the rapid growth of technology has created a gap between the availability of these technologies and the number of medical staff who can utilize them. Therefore, developing robust automated brain tumor detection techniques has become a major focus of research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Rajat Mehrotra, M. A. Ansari, Rajeev Agrawal, Hisham Al-Ward, Pragati Tripathi, Jay Singh
Summary: The histological study of brain biopsy specimens is currently used for diagnosing brain tumors, but it has drawbacks such as invasiveness and potential mistakes. By developing a completely computerized process using discrete wavelet transform, deep convolutional network, and machine learning, the burden on radiologists can be significantly reduced. The proposed model achieved a high accuracy of 99.5% in identifying tumorous and non-tumorous MR images, making it a valuable tool for assisting medical experts in BT identification.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Software Engineering
Wadhah Ayadi, Imen Charfi, Wajdi Elhamzi, Mohamed Atri
Summary: A new technique is proposed to improve the quality of MRI and classify brain tumors, achieving an accuracy of 90.27% and surpassing previous methods.
Article
Engineering, Biomedical
Zahra Sobhaninia, Nader Karimi, Pejman Khadivi, Shadrokh Samavi
Summary: This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhancement and tumor region detection. A network called Multiscale Cascaded Multitask Network is proposed, which is based on a multitask learning approach containing segmentation and classification tasks. The proposed method achieves high accuracy in both segmentation (96.27 and 95.88 for DCS and mean IoU, respectively) and classification (97.988 accuracy).
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Chemistry, Analytical
Muhannad Faleh Alanazi, Muhammad Umair Ali, Shaik Javeed Hussain, Amad Zafar, Mohammed Mohatram, Muhammad Irfan, Raed AlRuwaili, Mubarak Alruwaili, Naif H. Ali, Anas Mohammad Albarrak
Summary: This study proposes a novel transfer deep-learning model for early diagnosis of brain tumors and their subclasses, achieving high accuracy rates of 95.75% and 96.89% on MRI images from the same machine and an unseen dataset, respectively. The proposed model shows potential for aiding doctors and radiologists in diagnosing brain tumors early.
Article
Computer Science, Artificial Intelligence
Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter, Md. Alamgir Jalil Pramanik, Sunil Aryal, Muhammad Ali Abdulllah Almoyad, Khondokar Fida Hasan, Mohammad Ali Moni
Summary: Brain tumors are fatal and devastating, reducing life expectancy significantly. Accurate diagnosis is crucial for treatment plans. Manual analysis of MRI data is challenging and time-consuming, calling for a reliable deep learning model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Neurosciences
Theodoros N. Papadomanolakis, Eleftheria S. Sergaki, Andreas A. Polydorou, Antonios G. Krasoudakis, Georgios N. Makris-Tsalikis, Alexios A. Polydorou, Nikolaos M. Afentakis, Sofia A. Athanasiou, Ioannis O. Vardiambasis, Michail E. Zervakis
Summary: This study develops a diagnostic framework based on the combination of CNN and DWT for the diagnosis of brain gliomas. Experimental results show promising performance, with higher accuracy and sensitivity compared to traditional pixel values.
Article
Medicine, General & Internal
Venkatesan Rajinikanth, P. M. Durai Raj Vincent, C. N. Gnanaprakasam, Kathiravan Srinivasan, Chuan-Yu Chang
Summary: This research aims to develop an efficient deep-learning-based brain-tumor detection scheme using FLAIR- and T2-modality MRI slices. The scheme includes preprocessing, deep-feature extraction, tumor segmentation, feature optimization, and binary classification. Experimental results show that the integrated feature-based scheme achieves a classification accuracy of 99.6667% when using a support-vector-machine classifier.
Article
Computer Science, Information Systems
Yildiray Anagun
Summary: Early diagnosis of cancer is crucial and computer-aided intelligent systems can assist in the diagnosis. This study proposes a CNN-based brain tumor diagnosis system that achieves excellent results through improved architecture and optimization methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Health Care Sciences & Services
Mohamed Ait Amou, Kewen Xia, Souha Kamhi, Mohamed Mouhafid
Summary: In this study, a Bayesian Optimization-based efficient hyperparameter optimization technique for CNN is proposed to identify different types of brain tumors. The optimized CNN outperforms several well-known deep pre-trained models in terms of validation accuracy, demonstrating the feasibility of automating hyperparameter optimization.
Article
Biology
Ayan Mondal, Vimal K. Shrivastava
Summary: This article introduces a CNN-based brain tumor classification model and proposes a parametric activation function to improve its performance. The results show that the model achieves high accuracy on two brain tumor datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Wadhah Ayadi, Wajdi Elhamzi, Imen Charfi, Mohamed Atri
Summary: Brain tumor is a fatal cancer with various types, and accurate classification is crucial. MRI imaging is effective in distinguishing brain tumors, and deep convolutional neural networks have made great progress in this area. The proposed model for brain tumor classification shows convincing performance in experiments.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Nacer Farajzadeh, Nima Sadeghzadeh, Mahdi Hashemzadeh
Summary: Detecting brain tumors is crucial for patients' survival, and Magnetic Resonance Imaging (MRI) has been proven to be the most accurate method. However, the accuracy of evaluation by human specialists can be compromised due to fatigue, lack of expertise, and insufficiency of information in the images. This study proposes a segmentation approach to assist specialists in accurately detecting brain tumors, achieving the highest accuracy compared to previous studies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Instruments & Instrumentation
Adam Fauzi, Yuyun Yueniwati, Agus Naba, Rachmi Fauziah Rahayu
Summary: This study examines the influence of MRI sequences on the classification performance of DL techniques for distinguishing between MPBT and MBT. The results show that DL models, particularly ResNet101 and VGG19, demonstrate promising performance in classifying MPBT and MBT based on MRI images. The choice of MRI sequence can impact the sensitivity of tumor detection.
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Yahia Said, Mohammad Barr, Taoufik Saidani, Mohamed Atri
Summary: Desertification has become a global threat, especially in Middle Eastern countries like Saudi Arabia. Makkah, one of the most important cities in Saudi Arabia, needs protection from desertification. This paper proposes an automatic desertification detection system based on Deep Learning techniques, using Convolutional Neural Networks to classify aerial images and detect land state variation in real-time.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Yassin Kortli, Souhir Gabsi, Maher Jridi, Ayman Alfalou, Mohamed Atri
Summary: The paper discusses the use of the 2D-FFT algorithm in security and biometrics systems, emphasizing the impact of hardware/software co-design on reducing processing time and power consumption. An innovative architecture for the 2D-FFT algorithm is proposed and tested on the Zynq Soc platform for improved efficiency.
INTEGRATION-THE VLSI JOURNAL
(2022)
Article
Physics, Condensed Matter
Faouzi Nasri, Sirine Glayed, Nejeh Jaba, Abir Mera, Mohamed Atri, Mohsen Machhout
Summary: In this study, a mathematical methodology was used to investigate the electrical performance and thermal stability of 14-nm Bulk and SOI FinFET components. The results showed good agreement between the proposed model and experimental data as well as TCAD simulator results. It was also found that the 14 nm Bulk FinFET exhibited better temperature distribution than the 14 nm SOI FinFET after 100 ns.
MICRO AND NANOSTRUCTURES
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Hallek, Hamdi Boukamcha, Abdellatif Mtibaa, Mohamed Atri
Summary: This study introduces a new stereo matching algorithm that utilizes pixel-level difference adjustment, gradient matching, and rank transform, combined with guided filter aggregation of cost metrics and dynamic programming for disparity calculation to improve accuracy and runtime of the disparity map. Mean-shift image segmentation and refinement techniques are also used to enhance accuracy. The algorithm achieves high disparity evaluation speed and ranks third in accuracy and runtime on the Middlebury stereo benchmark.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Wajdi Elhamzi, Wadhah Ayadi, Mohamed Atri
Summary: This article introduces a new brain tumor segmentation method based on deep learning, which uses Convolutional Neural Networks to automatically and accurately segment MRI images. The method shows good performance in the tests, with high segmentation accuracy for different tumor regions.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics
Elham M. M. Al-Ali, Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. H. Laatar, Mohamed Atri
Summary: Green energy is crucial for sustainable development and environmental preservation in new cities. Accurate forecasting of solar energy production is a challenge, but the recent advancements in Artificial Intelligence, especially Deep Learning, have shown great promise in short-term time-series forecasting. In this study, a hybrid CNN-LSTM-Transformer model was used for solar energy production forecasting, with clustering analysis and feature selection techniques applied to the input data. The experimental results using the Fingrid open dataset demonstrated the efficiency and accuracy of the proposed model, surpassing existing models and combinations like LSTM-CNN. This reliable forecasting technique can facilitate the integration of solar energy into grids.
Article
Computer Science, Hardware & Architecture
Ahmed Ben Atitallah, Yahia Said, Mohamed Amin Ben Atitallah, Mohammed Albekairi, Khaled Kaaniche, Turki M. Alanazi, Sahbi Boubaker, Mohamed Atri
Summary: A new obstacle detection system based on an enhanced YOLO v5 neural network is proposed, which integrates DenseNet into the YOLO v5 backbone to improve the speed and accuracy of the network. Two compression techniques, channel pruning and quantization, are applied to ensure the embedded implementation of the system on a ZCU 102 board. The suggested system shows very encouraging results in terms of detection accuracy (83.42%) and detection speed (43 FPS).
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Biology
Zenglin Qiao, Lynn Li, Xinchao Zhao, Lei Liu, Qian Zhang, Shili Hechmi, Mohamed Atri, Xiaohua Li
Summary: With the development and maturity of machine learning methods, this paper proposes an improved Runge Kutta optimizer called GORUN to adaptively adjust the hyperparameters and improve the performance of machine learning methods for medical diagnosis. The proposed method has been validated against benchmark functions and demonstrated superior performance compared to other optimizers. It has also been employed to optimize Kernel Extreme Learning Machine and Residual Neural Networks, resulting in robust models for medical diagnosis. Experimental results on multiple medical datasets have shown its superiority.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Imaging Science & Photographic Technology
Mohamed Hallek, Randa Khemiri, Ali Algarwi, Abdellatif Mtibaa, Mohamed Atri
Summary: This paper presents a stereo-matching algorithm that produces high-quality disparity maps in real-time. The algorithm uses three per-pixel difference measurements and an improved dynamic programming method to optimize the disparity calculation. It achieves a processing speed of 98 frames per second and ranks fourth in accuracy and runtime in the Middlebury stereo benchmark.
IMAGING SCIENCE JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Wadhah Ayadi, Wajdi Elhamzi, Mohamed Atri
Summary: In this paper, a novel Convolutional Neural Network (CNN) scheme for glioma segmentation was proposed. The suggested framework consists of intensity normalization, automatic segmentation based on CNN, and post-processing. The technique was tested using public datasets and showed excellent performance in segmenting glioma tumors.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Mouna Afif, Riadh Ayachi, Yahia Said, Mohamed Atri
Summary: Since 2019, COVID-19 has caused significant damage globally and has become a serious health issue. The number of infected and confirmed cases continues to rise and healthcare systems are struggling to cope. This study proposes a deep learning-based application for COVID-19 segmentation and analysis, using lung and chest CT images. The developed system, based on a context aggregation neural network, shows superior performance compared to existing methods in accurately detecting COVID-19-related regions. The results demonstrate the effectiveness of the proposed work with an accuracy rate over 96.23%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics
Yahia Said, Mohamed Atri, Marwan Ali Albahar, Ahmed Ben Atitallah, Yazan Ahmad Alsariera
Summary: Significant progress has been made recently in technology. An intelligent system for assisting visually impaired people in navigation, particularly through indoor scene recognition, was proposed in this paper using deep learning techniques. Extensive experiments on the MIT67 dataset demonstrated the superiority of the proposed technique compared to the state-of-the-art.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Wadhah Ayadi, Wajdi Elhamzi, Mohamed Atri
Summary: Brain tumor segmentation is a challenging task, especially for gliomas. Researchers propose a new deep Convolutional Neural Network (CNN) architecture to enhance the segmentation results through pre-processing, automatic segmentation model, and post-processing methods.
PROCEEDINGS OF THE 2022 5TH INTERNATIONAL CONFERENCE ON ADVANCED SYSTEMS AND EMERGENT TECHNOLOGIES IC_ASET'2022)
(2022)
Article
Computer Science, Software Engineering
Wadhah Ayadi, Imen Charfi, Wajdi Elhamzi, Mohamed Atri
Summary: A new technique is proposed to improve the quality of MRI and classify brain tumors, achieving an accuracy of 90.27% and surpassing previous methods.
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
