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Search Results (19)
  • Open Access


    A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

    Faiz Ali Shah1, Muhammad Attique Khan2, Muhammad Sharif1, Usman Tariq3, Aimal Khan4, Seifedine Kadry5, Orawit Thinnukool6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1491-1507, 2022, DOI:10.32604/cmc.2022.019490

    Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms of different fruit diseases and… More >

  • Open Access


    Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

    Asif Mehmood1, Muhammad Attique Khan2, Usman Tariq3, Chang-Won Jeong4, Yunyoung Nam5,*, Reham R. Mostafa6, Amira ElZeiny7

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250

    Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained… More >

  • Open Access


    A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

    Khadija Manzoor1, Fiaz Majeed2, Ansar Siddique2, Talha Meraj3, Hafiz Tayyab Rauf4,*, Mohammed A. El-Meligy5, Mohamed Sharaf6, Abd Elatty E. Abd Elgawad6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1617-1630, 2022, DOI:10.32604/cmc.2022.018621

    Abstract Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized… More >

  • Open Access


    Multi-Layered Deep Learning Features Fusion for Human Action Recognition

    Sadia Kiran1, Muhammad Attique Khan1, Muhammad Younus Javed1, Majed Alhaisoni2, Usman Tariq3, Yunyoung Nam4,*, Robertas Damaševičius5, Muhammad Sharif6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800

    Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is… More >

  • Open Access


    Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

    Muhammad Rizwan Latif1, Muhamamd Attique Khan1, Muhammad Younus Javed1, Haris Masood2, Usman Tariq3, Yunyoung Nam4,*, Seifedine Kadry5

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364

    Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their… More >

  • Open Access


    An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

    Ahsan Aziz1, Muhammad Attique1, Usman Tariq2, Yunyoung Nam3,*, Muhammad Nazir1, Chang-Won Jeong4, Reham R. Mostafa5, Rasha H. Sakr6

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2653-2670, 2021, DOI:10.32604/cmc.2021.018606

    Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of… More >

  • Open Access


    Gastric Tract Disease Recognition Using Optimized Deep Learning Features

    Zainab Nayyar1, Muhammad Attique Khan1, Musaed Alhussein2, Muhammad Nazir1, Khursheed Aurangzeb2, Yunyoung Nam3,*, Seifedine Kadry4, Syed Irtaza Haider2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2041-2056, 2021, DOI:10.32604/cmc.2021.015916

    Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are… More >

  • Open Access


    Computer Decision Support System for Skin Cancer Localization and Classification

    Muhammad Attique Khan1, Tallha Akram2, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307

    Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >

  • Open Access


    Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion

    Yuyang Sun1, Peizhou Yan2, *, Zhengzheng Li2, Jiancheng Zou3, Don Hong4

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1563-1574, 2020, DOI:10.32604/cmc.2020.09763

    Abstract Real-time detection of driver fatigue status is of great significance for road traffic safety. In this paper, a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock. The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard. The landmarks of the driver’s face were labeled and the eye-area wassegmented. By calculating the aspect ratios of the eyes, the duration of eye closure, frequency of blinks and PERCLOS of both colored and infrared, fatigue can be detected. Based on the change of… More >

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