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  • Open Access

    ARTICLE

    Liver-Tumor Detection Using CNN ResUNet

    Muhammad Sohaib Aslam1, Muhammad Younas1, Muhammad Umar Sarwar1, Muhammad Arif Shah2,*, Atif Khan3, M. Irfan Uddin4, Shafiq Ahmad5, Muhammad Firdausi5, Mazen Zaindin6

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151

    Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies… More >

  • Open Access

    ARTICLE

    Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

    Gulzar Ahmad1, Saad Alanazi2, Madallah Alruwaili2, Fahad Ahmad3,6, Muhammad Adnan Khan4,*, Sagheer Abbas1, Nadia Tabassum5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2585-2600, 2021, DOI:10.32604/cmc.2021.015080

    Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More >

  • Open Access

    ARTICLE

    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli1, Tahira Nazir2,*, Aun Irtaza2, Ali Javed3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691

    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More >

  • Open Access

    ARTICLE

    Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

    Jingming Xia1, Yi Lu1, Ling Tan2,*, Ping Jiang3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 613-624, 2021, DOI:10.32604/cmc.2021.013457

    Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm,… More >

  • Open Access

    ARTICLE

    Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

    Hyun Kyu Shin1, Si Woon Lee2, Goo Pyo Hong3, Lee Sael2, Sang Hyo Lee4, Ha Young Kim5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170

    Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that… More >

  • Open Access

    ARTICLE

    Identification of Thoracic Diseases by Exploiting Deep Neural Networks

    Saleh Albahli1, Hafiz Tayyab Rauf2,*, 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 >

  • Open Access

    ARTICLE

    An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

    Sitara Afzal1, Muazzam Maqsood1,*, Irfan Mehmood2, Muhammad Tabish Niaz3, Sanghyun Seo4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2301-2315, 2021, DOI:10.32604/cmc.2021.013966

    Abstract Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is… More >

  • Open Access

    ARTICLE

    HGG-CNN: The Generation of the Optimal Robotic Grasp Pose Based on Vision

    Shiyin Qiu1,*, David Lodder2, Feifan Du2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1517-1529, 2020, DOI:10.32604/iasc.2020.012144

    Abstract Robotic grasping is an important issue in the field of robot control. In order to solve the problem of optimal grasping pose of the robotic arm, based on the Generative Grasping Convolutional Neural Network (GG-CNN), a new convolutional neural network called Hybrid Generative Grasping Convolutional Neural Network (HGG-CNN) is proposed by combining three small network structures called Inception Block, Dense Block and SELayer. This new type of convolutional neural network structure can improve the accuracy rate of grasping pose based on the GG-CNN network, thereby improving the success rate of grasping. In addition, the HGG-CNN convolutional neural network structure can… More >

  • Open Access

    ARTICLE

    Multi Criteria Decision Making System for Parking System

    Manjur Kolhar*, Abdalla Alameen

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 101-116, 2021, DOI:10.32604/csse.2021.014915

    Abstract System supported smart parking can reduce traffic by making it stress free to locate empty parking spaces, hence lowering the risk of unfocussed driving. In this study, we propose a smart parking system using deep learning and an application-based approach. This system has two modules, one module detects and recognizes a license plate (LP), and the other selects a parking space; both modules use deep learning techniques. We used two modules that work independently to detect and recognize an LP by using an image of the vehicle. To detect parking space, only deep learning techniques were used. The two modules… More >

  • Open Access

    ARTICLE

    Hajj Crowd Management Using CNN-Based Approach

    Waleed Albattah1,*, Muhammad Haris Kaka Khel2, Shabana Habib1, Muhammad Islam3, Sheroz Khan3,4, Kushsairy Abdul Kadir2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2183-2197, 2021, DOI:10.32604/cmc.2020.014227

    Abstract Hajj as the Muslim holy pilgrimage, attracts millions of humans to Mecca every year. According to statists, the pilgrimage has attracted close to 2.5 million pilgrims in 2019, and at its peak, it has attracted over 3 million pilgrims in 2012. It is considered as the world’s largest human gathering. Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided. This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj. The image classification… More >

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