Early Recognition of Skin Malignancy in Images Based on Convolutional Networks by Using Dynamic System Model. (17th February 2022)

• Record Type: Journal Article
• Authors: Ramesh, V.; Hamad, Abdulsattar Abdullah; Jwaid, Mohanad Fadhil; Sathyabama, M.; Abdulridha, Mustafa Mahdi; Kadhim, Noor Mohammed; Belay, Assaye
• Other Names: Velmurugan Palanivel Academic Editor.
• Is Part Of: Journal of nanomaterials. Volume 2022(2022)
• Publication Date: 2022-02-17
• Subjects: Nanostructured materials -- Periodicals; Nanotechnology -- Periodicals; Nanomatériaux; Nanostructured materials; Nanotechnology; Nanostructures; Nanotechnology; Periodicals; Fulltext; Internet Resources; Periodicals; 620.115
• DOI: 10.1155/2022/1754658
• Languages: English
• ISSNs: 1687-4110

Abstract

Abstract : Because of the high mortality rate, increased medical costs, and ongoing global growth in the incidence of this malignancy, early detection has become a top priority. Early detection and treatment of melanoma are critically important; the likelihood of a positive outcome rises dramatically. To address this issue, academic researchers plan to develop a prototype image analysis system based on deep learning to determine whether a lesion is malignant or benign based on dermatoscopy image databases. Pretrained convolutional networks with simple architectures were employed in this study to grasp their design better and to train the given dataset more quickly. Using convolutional neural networks as the basis, this research seeks to develop a deep learning system capable of classifying images. To train our model with the pretrained AlexNet, VGG, and ResNet networks, we will use the learning transfer methodology (or transfer learning), whose architecture we will outline so that it may subsequently be adjusted to our data. In this research work, fairly basic pretrained convolutional networks have been used to understand their architecture and efficiently train the given dataset. However, other networks have much more complex structures or even the same networks used, but with many more layers. For possible future work, it is proposed to use, for example, ResNet-152, Vgg-19, or other different networks such as DenseNet or Inception.