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  • 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

    Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

    Vatsala Anand1, Sheifali Gupta1, Deepika Koundal2,*, Shubham Mahajan3, Amit Kant Pandit3, Atef Zaguia4

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3145-3160, 2022, DOI:10.32604/cmc.2022.022788

    Abstract Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images… More >

  • Open Access

    ARTICLE

    Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

    Reham Alabduljabbar*, Hala Alshamlan

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 831-847, 2021, DOI:10.32604/cmc.2021.018402

    Abstract The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to extract the efficient feature. The… More >

  • Open Access

    ARTICLE

    Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

    Jehyeok Rew, Hyungjoon Kim, Eenjun Hwang*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 801-817, 2021, DOI:10.32604/cmc.2021.017892

    Abstract Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation… More >

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