S. Mohan Kumar^*, T. Kumanan

Computer Systems Science and Engineering, Vol.44, No.1, pp. 833-844, 2023, DOI:10.32604/csse.2023.022385

Abstract The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST’s sub-bands, energy features are extracted and stored in the feature database for training.… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265

Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph… More >

Juan Pablo Villa-Pulgarin¹, Anderson Alberto Ruales-Torres^1,2, 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-Arias^4,5, Oscar Cardona-Morales¹, Reinel Tabares-Soto^1,*

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 >

Javaria Tahir¹, Syed Rameez Naqvi^2,*, 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 >

Ye Tian¹, Liguo Zhang^1,2, Linshan Shen^1,*, 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 >