Muhammad Attique Khan¹, Abdul Majid¹, Nazar Hussain¹, Majed Alhaisoni², Yu-Dong Zhang³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3381-3399, 2021, DOI:10.32604/cmc.2021.014983

Abstract In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3 is utilized and retrained on… More >

Lihan Cui¹, Lijie Yan¹, Xiaohu Zhao¹, Lin Yuan², Jing Jin³, Jingcheng Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 621-634, 2021, DOI:10.32604/phyton.2021.015511

Abstract Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea stresses (i.e., the tea leafhopper,… More >

Wael Alosaimi^1,*, Hashem Alyami², M. Irfan Uddin³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950

Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >

Praveena Rajasekaran^*, Preetha Jaganathan, Anjali Annadurai

Computer Systems Science and Engineering, Vol.36, No.3, pp. 477-484, 2021, DOI:10.32604/csse.2021.014247

Abstract The stabilizing mechanisms of cervical spine spondylosis are involved in the degenerating segmentation vertebra, which often causes pain. Health of the individual is affected, both physically and mentally. Due to depression, nervousness, and psychological damages occur thereby losing their human activity functions. The nucleus pulposus of spinal disc herniation is prolapsed through a deficiency of annulus fibrosus. A jelly-like core part of the disc contains proteins that cause the tissues to become swollen when it touches the nucleus pulposus. The proposed Gradient Linear Classification (GLC) algorithm is used for the efficient automatic classification of disc degeneration herniation of Inter vertebral/… More >

Nakhim Chea¹, Yunyoung Nam^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390

Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual… More >

Saleh Albahli¹, Hafiz Tayyab Rauf^2,*, Muhammad Arif³, Md Tabrez Nafis⁴, Abdulelah Algosaibi⁵

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3139-3149, 2021, DOI:10.32604/cmc.2021.014134

Abstract With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN)… More >

Mangena Venu Madhavan¹, Dang Ngoc Hoang Thanh², Aditya Khamparia^1,*, Sagar Pande¹, Rahul Malik¹, Deepak Gupta³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466

Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More >

Yi Yang^1,2,* , Tao Li³

International Journal of Mental Health Promotion, Vol.22, No.4, pp. 249-259, 2020, DOI:10.32604/IJMHP.2020.014321

Abstract This study is aimed to explore relations between chronic diseases, activities of daily living (ADLs) disability and depression among older adults, particularly ADLs disability mediational effects on the correlation between chronic diseases and geriatric depression. Records of 5931 participants at age 60 and above from China Health and Retirement Longitudinal Study (CHARLS) in 2015 were used as valid samples for data relating to their chronic diseases index, ADLs scale and Center for Epidemiologic Studies Depression (CES-D) scale. Our findings confirm that among older adults, 75.9% are afflicted with chronic diseases and 52.5% have ADLs difficulty or disability, and that there… More >

Jalal Sadoon Hameed Al-bayati^1,*, Burak Berk Üstündağ²

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1479-1496, 2021, DOI:10.32604/cmc.2020.013329

Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay… More >

Inzamam Mashood Nasir¹, Asima Bibi², Jamal Hussain Shah², Muhammad Attique Khan¹, Muhammad Sharif², Khalid Iqbal³, Yunyoung Nam⁴, Seifedine Kadry^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945

Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >