Yousef Ibrahim Daradkeh^1,*, Volodymyr Gorokhovatskyi², Iryna Tvoroshenko², Medien Zeghid^3,4

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6069-6084, 2022, DOI:10.32604/cmc.2022.030254

Abstract The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: “object–etalon”, “object descriptor–etalon” and “vector description of the object–etalon”, which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for… More >

Mu Ye^1,2,3,4, Li Ji¹, Luo Tianye¹, Li Sihan⁵, Zhang Tong¹, Feng Ruilong¹, Hu Tianli^1,2,3,4, Gong He^1,2,3,4, Guo Ying^1,2,3,4, Sun Yu^1,2,3,4, Thobela Louis Tyasi⁶, Li Shijun^7,8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880

Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of the model to 98%. The… More >

Somenath Bera¹, Vimal K. Shrivastava², Suresh Chandra Satapathy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 219-250, 2022, DOI:10.32604/cmes.2022.020601

Abstract Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI, discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have shown huge success and offered great potential to yield high performance in HSI classification. Motivated by this successful performance, this… More >

B. Chellapraba^1,*, D. Manohari², K. Periyakaruppan³, M. S. Kavitha⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2353-2366, 2023, DOI:10.32604/iasc.2023.029923

Abstract Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed… 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

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) models, it is possible to… More >

Xiaoxia Yang¹, Yisheng Gao^2,*, Shuhua Zhang¹, Zhedong Ge¹, Yucheng Zhou¹

Journal of Renewable Materials, Vol.10, No.12, pp. 3587-3598, 2022, DOI:10.32604/jrm.2022.022300

Abstract Rosewood is a kind of high-quality and precious wood in China. The correct identification of rosewood species is of great significance to the import and export trade and species identification of furniture materials. In this paper, micro CT was used to obtain the micro images of cross sections, radial sections and tangential sections of 24 kinds of rosewood, and the data sets were constructed. PCA method was used to reduce the dimension of four features including logical binary pattern, local configuration pattern, rotation invariant LBP, uniform LBP. These four features and one feature not reducing dimension (rotation invariant uniform LBP)… More > Graphic Abstract

Vasumathi Devi Majety¹, N. Sharmili², Chinmaya Ranjan Pattanaik³, E. Laxmi Lydia⁴, Subhi R. M. Zeebaree⁵, Sarmad Nozad Mahmood⁶, Ali S. Abosinnee⁷, Ahmed Alkhayyat^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4393-4406, 2022, DOI:10.32604/cmc.2022.031109

Abstract Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based noise removal technique. Once the… More >

Radwa Marzouk¹, Eatedal Alabdulkreem², Sami Dhahbi³, Mohamed K. Nour⁴, Mesfer Al Duhayyim⁵, Mahmoud Othman⁶, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷, Ishfaq Yaseen⁷, Mohammed Rizwanullah⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3905-3920, 2022, DOI:10.32604/cmc.2022.029326

Abstract Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques. The proposed AIDTL-OCCM model involves… More >

Prasanalakshmi Balaji^1,*, B. Sri Revathi², Praveetha Gobinathan³, Shermin Shamsudheen³, Thavavel Vaiyapuri⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560

Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the most interesting research subjects in… More >

P. Immaculate Rexi Jenifer^1,*, S. Kannan²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1081-1097, 2023, DOI:10.32604/csse.2023.026128

Abstract Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed… More >