Article
Computer Science, Information Systems
Sunil Kumar, Dilip Kumar
Summary: The study highlights the significance of using neuroimaging methodologies for brain tumor segmentation and classification. Deep Learning has achieved remarkable success in this field, and the study demonstrates the superior performance of a convolutional neural network in classifying brain tumors. The proposed algorithm shows impressive results in the classification and segmentation of MRI brain images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
K. Sakthidasan Sankaran, M. Thangapandian, N. Vasudevan
Summary: This paper presents a method for detecting and categorizing brain tumors in MRI images, utilizing optimized clustering and feature selection techniques to effectively identify tumor regions, and using a deep neural network for classification. The developed approach achieved high accuracy rates compared to existing classifiers, demonstrating its effectiveness in tumor recognition and classification.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
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, 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
Engineering, Multidisciplinary
Imran Ul Haq, Haider Ali, Hong Yu Wang, Lei Cui, Jun Feng
Summary: Breast tumor segmentation is crucial for breast cancer detection, and an automated approach is needed to enhance efficiency and accuracy. This study proposes BTS-GAN, an automatic breast tumor segmentation process using cGAN in MRI scans. The method utilizes an encoder-decoder deep network, parallel dilated convolution module, and classification-related constraint to improve localization efficiency and convergence in image-to-image translation tasks. Experimental results show that BTS-GAN outperforms other segmentation techniques in terms of IoU and Dice coefficient, achieving an average score of 77% and 85% respectively.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Multidisciplinary Sciences
Wei-Wen Hsu, Jing-Ming Guo, Linmin Pei, Ling-An Chiang, Yao-Feng Li, Jui-Chien Hsiao, Rivka Colen, Peizhong Liu
Summary: Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. In this study, a novel hybrid CNN-based method using whole slide imaging (WSI) and multiparametric magnetic resonance imaging (mpMRI) is proposed for glioma subtype classification. The method overcomes the label constraint issue and enhances the robustness of predictions by fusing results from both modalities.
SCIENTIFIC REPORTS
(2022)
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
Computer Science, Information Systems
Bala Venkateswarlu Isunuri, Jagadeesh Kakarla
Summary: The study proposes an optimized model for the grade classification of brain magnetic resonance images, which utilizes efficientnet and coupled convolution network for feature extraction and enhancement, and employs a fully connected dense network for classification. Experimental results demonstrate that the proposed model achieves superior performance on larger datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ayesha Younis, Qiang Li, Mudassar Khalid, Beatrice Clemence, Mohammed Jajere Adamu
Summary: This study provides a brief review of the current state of the art in deep learning-based brain tumor segmentation and classification methods. It fills a gap in the literature and highlights the superior performance of deep learning models in brain tumor classification, as they can efficiently discover descriptive information about optimal brain tumor representation.
Article
Computer Science, Information Systems
V. V. S. Sasank, S. Venkateswarlu
Summary: The article discusses the importance of brain tumor classification in improving treatment outcomes, proposing a novel and efficient strategy for classifying brain MRI images into four grades. The method involves preprocessing, segmenting, extracting features, and utilizing a Hybrid Deep Neural Network for classification. This technique is compared with existing methods to demonstrate its overall efficiency.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Biomedical
Yan Dong, Ting Wang, Chiyuan Ma, Zhenxing Li, Ryad Chellali
Summary: In brain tumor segmentation, both high-precision local information and global contextual information are crucial. This paper proposes a brain tumor segmentation model called DE-Uformer, which utilizes both CNN encoder and Transformer encoder to extract local features and global representations. A nested encoder-aware feature fusion (NEaFF) module is introduced to effectively fuse the information from both encoders. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in brain tumor segmentation tasks.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
Article
Biology
U. Raghavendra, Anjan Gudigar, Aritra Paul, T. S. Goutham, Mahesh Anil Inamdar, Ajay Hegde, Aruna Devi, Chui Ping Ooi, Ravinesh C. Deo, Prabal Datta Barua, Filippo Molinari, Edward J. Ciaccio, U. Rajendra Acharya
Summary: A brain tumor is an abnormal mass inside the skull that can lead to significant health problems by putting pressure on the brain. Early detection of these tumors is crucial as malignant brain tumors grow rapidly and can result in higher mortality rates. Computer-aided diagnostic systems, combined with artificial intelligence techniques, play a vital role in the early detection of this disorder. This review highlights the challenges faced by CAD systems based on different modalities, current requirements in this field, and future prospects in research.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Neurosciences
Shao-Lun Lu, Heng-Chun Liao, Feng-Ming Hsu, Chun-Chih Liao, Feipei Lai, Furen Xiao
Summary: The ICTS dataset consists of contrast-enhanced T1-weighted images of 1500 patients, with tumors labeled by qualified neurosurgeons and radiation oncologists. This dataset is publicly available for ongoing benchmarking through an online evaluation system.
Article
Computer Science, Artificial Intelligence
Guanghua Xiao, Huibin Wang, Jie Shen, Zhe Chen, Zhen Zhang, Xiaomin Ge
Summary: Magnetic resonance imaging (MRI) is an effective non-invasive technique for diagnosing brain tumor diseases. Computer-aided diagnostic models based on deep convolutional neural networks have assisted in brain disease detection from MRIs. However, these models heavily rely on labeled data, which is prone to errors and time-consuming in practice. Self-supervised models show promise but suffer from high computing costs and inferior performance. Therefore, we propose a novel self-supervised framework for unsupervised classification of brain MRIs, improving performance while addressing these weaknesses.
NEURAL PROCESSING LETTERS
(2023)
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
Computer Science, Information Systems
Shuai Liu, Shuai Wang, Xinyu Liu, Jianhua Dai, Khan Muhammad, Amir H. H. Gandomi, Weiping Ding, Mohammad Hijji, Victor Hugo C. de Albuquerque
Summary: Computer vision, particularly visual monitoring technology, has shown great potential in the complex monitoring environment. This article proposes a fuzzy inference-based monitoring method that utilizes human inertial thinking characteristics to infer the target's location and applies an alternative selection strategy based on thinking set. Experimental results on multiple datasets demonstrate the effectiveness and robustness of the proposed method in IoT-assisted monitoring.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Review
Computer Science, Theory & Methods
Fath U. Min Ullah, Mohammad S. Obaidat, Amin Ullah, Khan Muhammad, Mohammad Hijji, Sung Wook Baik
Summary: Recent advancements in intelligent surveillance systems for video analysis have attracted significant attention in the research community. Automatic violence detection systems using artificial neural networks and machine intelligence are in high demand in heavily crowded areas to ensure safety and security in smart cities. Extensive literature on violence detection has been published, but existing surveys are limited in scope. To address this, we conduct a comprehensive survey and analysis of the literature, examining machine learning strategies, neural network-based analysis, limitations, and datasets. We also discuss evaluation strategies, metrics, and provide recommendations for future research in violence detection.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Arun Kumar Sangaiah, Khan Muhammad
Summary: Video capsule endoscopy (VCE) is a revolutionary technology for early diagnosis of gastric disorders, but manual interpretation of VCE videos is time-consuming due to the high redundancy and subtle manifestation of anomalies. Several machine learning methods have been adopted to improve VCE analysis, but their clinical impact is yet to be explored. This survey aimed to bridge the gap between existing ML-based research and clinically significant rules established by gastroenterologists. A framework for interpreting raw frames and merging findings with meta-data was proposed. The challenges and opportunities for VCE analysis were discussed, and the importance of maximizing the discriminative power of features, creating large datasets, and ensuring explainability and reliability of ML-based diagnostics in VCE was emphasized.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Physics, Applied
Sadaf Irshad, Muhammad Shakeel, Aysha Bibi, Muhammad Sajjad, Kottakkran Sooppy Nisar
Summary: This study investigates the optical soliton solutions to the fractional nonlinear Schrodinger equation with nonlinear oscillating coefficient and Beta and M-truncated derivatives. A complex wave transformation is applied to convert the fractional NLS equation into an ordinary differential equation. The optical solution structures are obtained using the SSE method. The fractional NLS equation is commonly used in various fields such as optical telecommunication, high-energy physics, gas dynamics, electrodynamics, and ocean engineering. The graphical representation of the results is also discussed in detail.
MODERN PHYSICS LETTERS B
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Kit Yan Chan, Bilal Abu-Salih, Khan Muhammad, Vasile Palade, Rifai Chai
Article
Computer Science, Information Systems
Khan Muhammad, Hayat Ullah, Mohammad S. Obaidat, Amin Ullah, Arslan Munir, Muhammad Sajjad, Victor Hugo C. de Albuquerque
Summary: This article proposes an efficient deep-learning-based framework for multiperson salient soccer event recognition in the IoT-enabled FinTech. The framework performs event recognition through frames preprocessing, frame-level discriminative features extraction, and high-level events recognition in soccer videos. The results validate the suitability of the proposed framework for salient event recognition in Nx-IoT environments.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Muhammad Irfan, Khan Muhammad, Muhammad Sajjad, Khalid Mahmood Malik, Faouzi Alaya Cheikh, Joel J. P. C. Rodrigues, Victor Hugo C. de Albuquerque
Summary: This article discusses the significant bandwidth consumption of immersive videos in industry 4.0 and proposes a solution using convolutional neural networks to select the user's region of interest and reduce bandwidth usage.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Naqqash Dilshad, Amin Ullah, Jaeho Kim, Jeongwook Seo
Summary: Object detection supported by UAVs has gained significant interest recently, with applications in surveillance, search and rescue, traffic, and disaster management. To address the challenge of GPS-restricted environments or GPS sensor failure, a novel location awareness framework called LocateUAV is proposed to detect UAV location in real time using a lightweight CNN. The framework involves object detection, optical character recognition, and map API integration.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Dan Wang, Bo Li, Bin Song, Yingjie Liu, Khan Muhammad, Xiaokang Zhou
Summary: In this article, a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework is proposed to achieve secure and reliable real-time computation. The framework combines digital twin with edge network and adopts blockchain technology. By utilizing a data and knowledge dual-driven learning solution, the communication and computation efficiency is improved. Experimental results demonstrate the efficiency and reliability of the proposed resource allocation scheme in the HDTIoT system.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Review
Engineering, Multidisciplinary
Muhammad Sajjad, Fath U. Min Ullah, Mohib Ullah, Georgia Christodoulou, Faouzi Alaya Cheikh, Mohammad Hijji, Khan Muhammad, Joel J. P. C. Rodrigues
Summary: Facial expression recognition (FER) is a complex research topic with applications in various fields, such as healthcare and security. Computational FER mimics human facial expression coding skills to assist human-computer interaction. This study thoroughly analyzes and surveys the existing literature on FER, highlights the working flow of FER methods, discusses limitations in existing surveys, investigates FER datasets, and comprehensively discusses measures to evaluate FER performance.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Mathematics
Mohammad Hijji, Hikmat Yar, Fath U. Min Ullah, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Khan Muhammad, Muhammad Sajjad
Summary: Nowadays, people prefer to use private transport due to its low cost, comfortable ride, and personal preferences, resulting in a reduction in the use of public transportation. However, the use of personal transport has led to numerous road accidents due to drivers' conditions such as drowsiness, stress, tiredness, and age. To address this issue, an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) was proposed to detect and identify different states of the driver. The system outperformed state-of-the-art techniques in experiments conducted on custom and publicly available datasets.
Article
Mathematics
Mohammad Hijji, Abbas Khan, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Muhammad Sajjad, Khan Muhammad
Summary: Due to the large distance and relative motion, vehicle license plate images are often low resolution and blurry. Traditional techniques have been developed to upgrade the low-quality images, but most studies focus on super-resolution rather than motion de-blurring. This work extends SRGAN by adding an intelligent motion-deblurring method (SRGAN-LP) and achieves improved results with higher quantitative and qualitative values.
Article
Automation & Control Systems
Tanveer Hussain, Fath U. Min Ullah, Samee Ullah Khan, Amin Ullah, Umair Haroon, Khan Muhammad, Sung Wook Baik, Victor Hugo C. de Albuquerque
Summary: Video summarization is important for suppressing high-dimensional video data. However, prior research has not focused on the need for surveillance video summarization, and mainstream techniques lack event occurrence detection. Therefore, we propose a two-fold 3-D deep learning-assisted framework for video summarization.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Muhammad Sajjad, Tariq Shah, Robinson Julian Serna
Summary: This article mainly introduces the nonlinear components (S-boxes) used in block ciphers and their role in improving the security of cryptographic systems. By designing a pair of S-boxes and conducting various tests, the nonlinear properties, strict avalanche criterion, linear approximation probability, and differential approximation probability are verified.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Mathematics, Applied
Muhammad Sajjad, Tariq Shah, Qin Xin, Bander Almutairi
Summary: This article provides an overview of the theory behind the Eisenstein field and its extension field, as well as a detailed framework for building BCH codes over the EF. It also discusses the decoding of these codes using the Berlekamp-Massey algorithm, and investigates their error-correcting capabilities and minimal distance expressions. The article offers researchers and engineers creating and implementing robust error-correcting codes for digital communication systems with valuable information on building, decoding, and performance assessment.
