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  • Open Access

    ARTICLE

    Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

    Amina Bibi1, Muhamamd Attique Khan1, Muhammad Younus Javed1, Usman Tariq2, Byeong-Gwon Kang3, Yunyoung Nam3,*, Reham R. Mostafa4, Rasha H. Sakr5

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2477-2495, 2022, DOI:10.32604/cmc.2022.018917

    Abstract Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation… More >

  • Open Access

    ARTICLE

    Optimized Convolutional Neural Network Models for Skin Lesion Classification

    Juan Pablo Villa-Pulgarin1, Anderson Alberto Ruales-Torres1,2, Daniel Arias-Garzón1, Mario Alejandro Bravo-Ortiz1, Harold Brayan Arteaga-Arteaga1, Alejandro Mora-Rubio1, Jesus Alejandro Alzate-Grisales1, Esteban Mercado-Ruiz1, M. Hassaballah3, Simon Orozco-Arias4,5, Oscar Cardona-Morales1, Reinel Tabares-Soto1,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529

    Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and… More >

  • Open Access

    ARTICLE

    A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

    Javaria Tahir1, Syed Rameez Naqvi2,*, Khursheed Aurangzeb3, Musaed Alhussein3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.018949

    Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two… More >

  • Open Access

    ARTICLE

    Big Data Analytics with OENN Based Clinical Decision Support System

    Thejovathi Murari1, L. Prathiba2, Kranthi Kumar Singamaneni3,*, D. Venu4, Vinay Kumar Nassa5, Rachna Kohar6, Satyajit Sidheshwar Uparkar7

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203

    Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

    C. S. S. Anupama1, L. Natrayan2, E. Laxmi Lydia3, Abdul Rahaman Wahab Sait4, José Escorcia-Gutierrez5, Margarita Gamarra6,*, Romany F. Mansour7

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1297-1313, 2022, DOI:10.32604/cmc.2022.018396

    Abstract Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images

    G. Reshma1,*, Chiai Al-Atroshi2, Vinay Kumar Nassa3, B.T. Geetha4, Gurram Sunitha5, Mohammad Gouse Galety6, S. Neelakandan7

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117

    Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker),… More >

  • Open Access

    ARTICLE

    Mixed Re-Sampled Class-Imbalanced Semi-Supervised Learning for Skin Lesion Classification

    Ye Tian1, Liguo Zhang1,2, Linshan Shen1,*, Guisheng Yin1, Lei Chen3

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 195-211, 2021, DOI:10.32604/iasc.2021.016314

    Abstract Skin cancer is one of the most common types of cancer in the world, melanoma is considered to be the deadliest type among other skin cancers. Quite recently, automated skin lesion classification in dermoscopy images has become a hot and challenging research topic due to its essential way to improve diagnostic performance, thus reducing melanoma deaths. Convolution Neural Networks (CNNs) are at the heart of this promising performance among a variety of supervised classification techniques. However, these successes rely heavily on large amounts of class-balanced clearly labeled samples, which are expensive to obtain for skin lesion classification in the real… More >

  • Open Access

    ARTICLE

    Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

    Rabia Javed1,2, Mohd Shafry Mohd Rahim1, Tanzila Saba3, Suliman Mohamed Fati3, Amjad Rehman3,*, Usman Tariq4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677

    Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the… More >

  • Open Access

    ARTICLE

    Classification and Research of Skin Lesions Based on Machine Learning

    Jian Liu1, Wantao Wang1, Jie Chen2, *, Guozhong Sun3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

    Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models… More >

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