R. Poonguzhali¹, Sultan Ahmad², P. Thiruvannamalai Sivasankar³, S. Anantha Babu³, Pranav Joshi⁴, Gyanendra Prasad Joshi⁵, Sung Won Kim^6,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816 - 22 September 2022

Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented… More >

Sukhendra Singh¹, Sur Singh Rawat², Manoj Gupta³, B. K. Tripathi⁴, Faisal Alanzi⁵, Arnab Majumdar⁶, Pattaraporn Khuwuthyakorn⁷, Orawit Thinnukool^7,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364 - 22 September 2022

Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and… More >

Syeda Naila Batool, Ghulam Gilanie^*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1319-1332, 2023, DOI:10.32604/cmc.2023.032121 - 22 September 2022

Abstract Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional… More >

Nora Abdullah Alkhaldi^1,*, Hanan T. Halawani²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 399-414, 2023, DOI:10.32604/cmc.2023.030872 - 22 September 2022

Abstract Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The 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 - 19 July 2022

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)… 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 >

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 - 15 June 2022

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… More >

Tariq Mohammed Alqahtani^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1433-1449, 2023, DOI:10.32604/csse.2023.025594 - 15 June 2022

Abstract In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of… More >

Romany F. Mansour^1,*, Eatedal Alabdulkreem²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1161-1169, 2023, DOI:10.32604/csse.2023.023307 - 15 June 2022

Abstract The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image More >

M. Rajakani^1,*, R. J. Kavitha², A. Ramachandran³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 265-280, 2023, DOI:10.32604/csse.2023.024994 - 01 June 2022

Abstract Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient More >