Farman Ali¹, Sadia Khan², Arbab Waseem Abbas², Babar Shah³, Tariq Hussain², Dongho Song^4,*, Shaker EI-Sappagh^5,6, Jaiteg Singh⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103

Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, More >

Mohd Anul Haq^*

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1031-1046, 2022, DOI:10.32604/csse.2022.023221

Abstract To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was… More >

Mesfer Al Duhayyim¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abdelzahir Abdelmaboud⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶, Mohammed Rizwanullah⁶, Radwa Marzouk^7,8

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5699-5715, 2022, DOI:10.32604/cmc.2022.024431

Abstract The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of… More >

Harmandeep Singh Gill^1,*, Osamah Ibrahim Khalaf², Youseef Alotaibi³, Saleh Alghamdi⁴, Fawaz Alassery⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5135-5150, 2022, DOI:10.32604/cmc.2022.022809

Abstract Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues. The performance of the classification scheme depends on the range of captured images, the volume of features, types of characters, choice of features from extracted features, and type of classifiers used. This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) application to classify the fruit images. Classification… More >

N. Veni^*, J. Manjula

Computer Systems Science and Engineering, Vol.42, No.2, pp. 825-835, 2022, DOI:10.32604/csse.2022.022318

Abstract The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is More >

Xiaoting Zhang¹, Weidong Gao^2,*, Ruru Pan²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1533-1546, 2022, DOI:10.32604/cmes.2022.018763

Abstract Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper… More >

Amna Mir¹, Umer Yasin¹, Salman Naeem Khan¹, Atifa Athar^3,*, Riffat Jabeen², Sehrish Aslam¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3733-3746, 2022, DOI:10.32604/cmc.2022.022524

Abstract Diabetic Retinopathy (DR) is a common complication of diabetes mellitus that causes lesions on the retina that affect vision. Late detection of DR can lead to irreversible blindness. The manual diagnosis process of DR retina fundus images by ophthalmologists is time consuming and costly. While, Classical Transfer learning models are extensively used for computer aided detection of DR; however, their maintenance costs limits detection performance rate. Therefore, Quantum Transfer learning is a better option to address this problem in an optimized manner. The significance of Hybrid quantum transfer learning approach includes that it performs heuristically.… More >

Rahul Sharma¹, Amar Singh¹, Kavita², N. Z. Jhanjhi³, Mehedi Masud⁴, Emad Sami Jaha⁵, Sahil Verma^2,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2125-2140, 2022, DOI:10.32604/cmc.2022.020017

Abstract Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like… More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition,… More >

S. Poovizhi, T. R. Ganesh Babu, R. Praveena^*

Molecular & Cellular Biomechanics, Vol.18, No.4, pp. 201-219, 2021, DOI:10.32604/mcb.2021.017984

Abstract Skin is the largest organ and outer enclosure of the integumentary system that protects the human body from pathogens. Among various cancers in the world, skin cancer is one of the most commonly diagnosed cancer which can be either melanoma or non-melanoma. Melanoma cancers are very fatal compared with non-melanoma cancers but the chances of survival rate are high when diagnosed and treated earlier. The main aim of this work is to analyze and investigate the performance of Non-Subsampled Bendlet Transform (NSBT) on various classifiers for detecting melanoma from dermoscopic images. NSBT is a multiscale More >