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Search Results (16)
  • Open Access

    ARTICLE

    Smart MobiNet: A Deep Learning Approach for Accurate Skin Cancer Diagnosis

    Muhammad Suleman1, Faizan Ullah1, Ghadah Aldehim2,*, Dilawar Shah1, Mohammad Abrar1,3, Asma Irshad4, Sarra Ayouni2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365

    Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from the International Skin Imaging Collaboration,… More >

  • Open Access

    ARTICLE

    Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

    C. S. S. Anupama1, Saud Yonbawi2, G. Jose Moses3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Jungeun Kim8,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2079-2095, 2023, DOI:10.32604/csse.2023.038322

    Abstract Skin cancer is one of the most dangerous cancer. Because of the high melanoma death rate, skin cancer is divided into non-melanoma and melanoma. The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions. Sometimes, pathology and biopsy examinations are required for cancer diagnosis. Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images. With recent advancements in hardware and software technologies, deep learning (DL) has developed as a potential technique for feature learning. Therefore, this study develops a new sand cat swarm optimization with a deep transfer learning method for… More >

  • Open Access

    ARTICLE

    Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

    K. Kalyani1, Sara A Althubiti2, Mohammed Altaf Ahmed3, E. Laxmi Lydia4, Seifedine Kadry5, Neunggyu Han6, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005

    Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC… More >

  • Open Access

    ARTICLE

    Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

    G. Murugesan1,*, J. Jeyapriya2, M. Hemalatha3, S. Rajeshkannan4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2099-2109, 2023, DOI:10.32604/iasc.2023.032068

    Abstract The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3,… More >

  • Open Access

    ARTICLE

    An Efficient Hybrid Optimization for Skin Cancer Detection Using PNN Classifier

    J. Jaculin Femil1,*, T. Jaya2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2919-2934, 2023, DOI:10.32604/csse.2023.032935

    Abstract The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life. The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment. Therefore, an effective image processing approach is employed in this present study for the accurate detection of skin cancer. Initially, the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with… More >

  • Open Access

    ARTICLE

    Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

    S. Ayshwarya Lakshmi1,*, K. Anandavelu2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3403-3413, 2023, DOI:10.32604/iasc.2023.030970

    Abstract Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection. The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process. Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties. One of the most significant requirements is to design optimal solutions to optimize their use. There is no particular technique that can answer all optimization issues. The proposed enhanced cuckoo search optimization method indicates… More >

  • Open Access

    ARTICLE

    MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

    Nithya Rekha Sivakumar1,*, Sara Abdelwahab Ghorashi1, Faten Khalid Karim1, Eatedal Alabdulkreem1, Amal Al-Rasheed2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6253-6267, 2022, DOI:10.32604/cmc.2022.029266

    Abstract Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these… More >

  • Open Access

    ARTICLE

    Hybrid Color Texture Features Classification Through ANN for Melanoma

    Saleem Mustafa1, Arfan Jaffar1, Muhammad Waseem Iqbal2,*, Asma Abubakar2, Abdullah S. Alshahrani3, Ahmed Alghamdi4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549

    Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

  • Open Access

    ARTICLE

    An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

    Mohamed W. Abo El-Soud1,2,*, Tarek Gaber2,3, Mohamed Tahoun2, Abdullah Alourani1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561

    Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for… More >

  • Open Access

    ARTICLE

    Hybridization of CNN with LBP for Classification of Melanoma Images

    Saeed Iqbal1,*, Adnan N. Qureshi1, Ghulam Mustafa2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4915-4939, 2022, DOI:10.32604/cmc.2022.023178

    Abstract Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible… More >

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