Walid El-Shafai^1,2, Amira A. Mahmoud¹, Anas M. Ali^1,3, El-Sayed M. El-Rabaie¹, Taha E. Taha¹, Osama F. Zahran¹, Adel S. El-Fishawy¹, Naglaa F. Soliman⁴, Amel A. Alhussan^5,*, Fathi E. Abd El-Samie¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3795-3814, 2022, DOI:10.32604/cmc.2022.029134 - 16 June 2022

Abstract In the literature, numerous techniques have been employed to decrease noise in medical image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). These techniques are organized into two main classes: the Multiple Image (MI) and the Single Image (SI) techniques. In the MI techniques, images usually obtained for the same area scanned from different points of view are used. A single image is used in the entire procedure in the SI techniques. SI denoising techniques can be carried out both in a transform or spatial… More >

Kriti Mahajan¹, Urvashi Garg¹, Nitin Mittal², Yunyoung Nam³, Byeong-Gwon Kang^4,*, Mohamed Abouhawwash^5,6

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3705-3720, 2022, DOI:10.32604/cmc.2022.027936 - 16 June 2022

Abstract Spectral unmixing is essential for exploitation of remotely sensed data of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members and their matching fractional, draft rules abundances for every pixel in HSI. This paper aims to unmix hyperspectral data using the minimal volume method of elementary scrutiny. Moreover, the problem of optimization is solved by the implementation of the sequence of small problems that are constrained quadratically. The hard constraint in the final step for the abundance fraction is then replaced with… More >

Xiaowei Gu^1,#, Shuwen Chen^1,2,#,*, Huisheng Zhu¹, Mackenzie Brown^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 507-530, 2022, DOI:10.32604/cmes.2022.018948 - 15 June 2022

Abstract Coronavirus disease 2019 brings a huge burden on the medical industry all over the world. In the background of artificial intelligence (AI) and Internet of Things (IoT) technologies, chest computed tomography (CT) and chest X-ray (CXR) scans are becoming more intelligent, and playing an increasingly vital role in the diagnosis and treatment of diseases. This paper will introduce the segmentation of methods and applications. CXR and CT diagnosis of COVID-19 based on deep learning, which can be widely used to fight against COVID-19. 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 >

Mahmoud Ragab^1,2,3,*, Hesham A. Abdushkour⁴, Alaa F. Nahhas⁵, Wajdi H. Aljedaibi⁶

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 533-546, 2022, DOI:10.32604/cmc.2022.028856 - 18 May 2022

Abstract Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network… More >

Jahangir Jabbar¹, Muzammil Hussain², Hassaan Malik^2,*, Abdullah Gani³, Ali Haider Khan², Muhammad Shiraz⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1827-1844, 2022, DOI:10.32604/cmc.2022.024965 - 18 May 2022

Abstract Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection… More >

Khadija Khan, Samabia Tehsin^*

Journal on Artificial Intelligence, Vol.4, No.1, pp. 1-13, 2022, DOI:10.32604/jai.2022.028092 - 16 May 2022

Abstract Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood cells. Physicians rely on their experience to determine the type and subtype of Leukemia from the blood sample. Most people are misdiagnosed when it comes to its subtypes, the error rates can be up to 40% during the classification process. That too depends on the expertise of the physician. This research represents a Convolutional Neural Network based medical image classifier. The proposed technique can classify Leukemia and its five subtypes. More >

WEIYU CHEN^1,2, MUHSIN H. YOUNIS³, ZHONGKUO ZHAO^1,*, WEIBO CAI^3,*

BIOCELL, Vol.46, No.8, pp. 1789-1801, 2022, DOI:10.32604/biocell.2022.018197 - 22 April 2022

Abstract The knowledge of interactions among functional proteins helps researchers understand disease mechanisms and design potential strategies for treatment. As a general approach, the fluorescent and affinity tags were employed for exploring this field by labeling the Protein of Interest (POI). However, the autofluorescence and weak binding strength significantly reduce the accuracy and specificity of these tags. Conversely, HaloTag, a novel self-labeling enzyme (SLE) tag, could quickly form a covalent bond with its ligand, enabling fast and specific labeling of POI. These desirable features greatly increase the accuracy and specificity, making the HaloTag a valuable system More >

Elias Hossain¹, Md. Shazzad Hossain², Md. Selim Hossain³, Sabila Al Jannat⁴, Moontahina Huda⁵, Sameer Alsharif⁶, Osama S. Faragallah⁷, Mahmoud M. A. Eid⁸, Ahmed Nabih Zaki Rashed^9,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977 - 21 April 2022

Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused… More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057 - 29 March 2022

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram More >