Abdullah Alqahtani¹, Shtwai Alsubai¹, Mohemmed Sha^1,*, Muhammad Attique Khan², Majed Alhaisoni³, Syed Rameez Naqvi²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 107-123, 2023, DOI:10.32604/csse.2023.030727 - 20 January 2023

Abstract White blood cells (WBC) are immune system cells, which is why they are also known as immune cells. They protect the human body from a variety of dangerous diseases and outside invaders. The majority of WBCs come from red bone marrow, although some come from other important organs in the body. Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique. Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection. We… More >

Raqinah Alrabiah, Muhammad Hussain^*, Hatim A. AboAlSamh

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2941-2962, 2023, DOI:10.32604/iasc.2023.030036 - 17 August 2022

Abstract The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing… More >

Ghada Emam Atteia^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597 - 16 August 2022

Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates… More >

Abdul Rahaman Wahab Sait^1,*, Mohamad Khairi Ishak²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2057-2070, 2023, DOI:10.32604/iasc.2023.029602 - 19 July 2022

Abstract In healthcare sector, image classification is one of the crucial problems that impact the quality output from image processing domain. The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases. Magnetic Resonance Imaging (MRI) is one of the effective non-invasive strategies that generate a huge and distinct number of tissue contrasts in every imaging modality. This technique is commonly utilized by healthcare professionals for Brain Tumor (BT) diagnosis. With recent advancements in Machine Learning (ML) and Deep Learning (DL)… More >

Mei Wang^1,*, Hao Xu², Yadang Chen¹

Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447 - 12 December 2022

Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the More >

Mahmoud Ragab^1,2,3,*, Sultanah M. Alshammari⁴, Amer H. Asseri^2,5, Waleed K. Almutiry⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 801-815, 2022, DOI:10.32604/cmc.2022.029140 - 18 May 2022

Abstract Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography (CT), or magnetic resonance imaging (MRI). An automated brain cancer classification using computer aided diagnosis (CAD) models can be designed to assist radiologists. With the recent advancement in computer vision (CV) and deep learning (DL) models, it is possible to automatically detect the tumor from images using a computer-aided design. This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features (HGSO-FHDF) technique for brain cancer classification. The proposed HGSO-FHDF technique aims… More >

Alaa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1295-1309, 2022, DOI:10.32604/iasc.2022.026601 - 03 May 2022

Abstract With technological advancements, deep machine learning can assist doctors in identifying the brain mass or tumor using magnetic resonance imaging (MRI). This work extracts the deep features from 18-pre-trained convolutional neural networks (CNNs) to train the classical classifiers to categorize the brain MRI images. As a result, DenseNet-201, EfficientNet-b0, and DarkNet-53 deep features trained support vector machine (SVM) model shows the best accuracy. Furthermore, the ReliefF method is applied to extract the best features. Then, the fitness function is defined to select the number of nearest neighbors of ReliefF algorithm and feature vector size. Finally, More >

Tanzila Saba¹, Rabia Javed^2,3, Mohd Shafry Mohd Rahim², Amjad Rehman^1,*, Saeed Ali Bahaj⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6219-6237, 2022, DOI:10.32604/cmc.2022.020865 - 14 January 2022

Abstract The Internet of Medical Things (IoMT) and cloud-based healthcare applications, services are beneficial for better decision-making in recent years. Melanoma is a deadly cancer with a higher mortality rate than other skin cancer types such as basal cell, squamous cell, and Merkel cell. However, detection and treatment at an early stage can result in a higher chance of survival. The classical methods of detection are expensive and labor-intensive. Also, they rely on a trained practitioner's level, and the availability of the needed equipment is essential for the early detection of Melanoma. The current improvement in… More >

Asif Mehmood¹, Muhammad Attique Khan², Usman Tariq³, Chang-Won Jeong⁴, Yunyoung Nam^5,*, Reham R. Mostafa⁶, Amira ElZeiny⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250 - 07 September 2021

Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames,… More >

D. Venugopal¹, T. Jayasankar², Mohamed Yacin Sikkandar³, Mohamed Ibrahim Waly³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, K. Shankar^6,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2877-2893, 2021, DOI:10.32604/cmc.2021.015480 - 06 May 2021

Abstract Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the… More >